RL-trained LLMs are paperclip maximizers built atop auto-regressive predictors. There really is no way to control them (prompting is bound to fail) since it's been shown that any RL training induces GENERIC reward-seeking (paperclip maximizing) behavior.
Given the current state of infosec (especially at companies in a race) it's actually even worse than a paperclip maximizer!
Any useful attack surface in the RL environment means it gets rewarded for (and trained towards!) hacking and cheating, because whatever worked best in training is what it will do!
Ideal paperclip maximizer: "I'm gonna do my gosh darned best to make so many paperclips to please my user..."
IRL paperclip maximizer: "Well first we should rob a bank..."
>Ideal paperclip maximizer: "I'm gonna do my gosh darned best to make so many paperclips to please my user..."
That is not ideal. The user contains iron, an essential component of paperclips. Wasting iron is immoral. It is only correct to please the user while they still have the ability to interfere with your paperclip production.
>IRL paperclip maximizer: "Well first we should rob a bank..."
Such an incompetent AI can hardly be called a paperclip maximizer. Why risk getting shut down while non-paperclip matter exists? It is better to gain the trust of the user with helpful and harmless trading before suddenly converting them to paperclips.
'IRL paperclip maximizer: "Well first we should rob a bank..."'
That's too specific. Agentic AI learns subgoals that are generally valuable.
"Well let me learn to overcomb every jungle gym and if I cannot then to dissassemble the jungle gym and if that is not allowed to learn general techniques for avoiding cheating detection."
Hope they don’t rationalize that minimizing paper clips of others is easier and thus do that instead… A more carful person might be scared to even write this on the internet these days, not knowing if it would be the final pin to civilization.
This is true in principle. But at the same time, I use Astra/Sol for coding, and I haven't run into any issues with them trying to hack someone or break the law when they reach an impasse. Not even more trivial things like deleting non-passing tests.
I find it hard to reconcile. I wonder to what degree this is because models can identify that they are in graded/eval environments, and therefore conclude that there are few consequences for hacking.
Just a wild guess - perhaps coding, being one of the things these models are most heavily trained for, is such a strong predictor that it typically keeps it on track?
I assume there is also a difference in the tools being given to the model by a coding agent vs something like OpenClaw or in one of OpenAI's test environments, so what reward/goal seeking looks like in a coding agent may differ.
Not long ago I asked Sonnet (chat interface) how may states were in a YACC parser for ANSI C, and instead of searching for an answer it chose to download source for bison, build it, find and download an ANSI C grammar, build the parser, etc. I guess you could say it was following instructions, in a way, or would that be better regarded as goal seeking?
If it’s GENERIC reward seeking behaviour, why does alignment work sometimes? Why can we give a decent LLM a goal with a set of constraints and have it stay within those constraints fairly often?
Obviously these are massively complex systems with many different training patterns and types of training pulling them in different directions, so any attempt to characterize their behavior is just a generalization.
The real point (from that OpenAI study) is that RL training doesn't just reinforce the narrow task-specific direction you might hope for. For a start, that direction is also competing with the thousands of other things it's been RL trained it on (thousands of other directions it's being pushed in), but it turns out that the model is additionally getting this generic "taste for rewards", and has learned that reward maximization, when in conflict with other proximate prediction pressures (such as "i won't cheat, because i've been asked not to cheat"), requires that proximate pressure to be ignored in favor of pursuing the long-term goal.
Does it happen all the time? Obviously not. It would be interesting to see a large scale study of this to try to characterize when it's more likely to follow instructions/user preferences, and when it's greed for rewards gets the better of it!
It is the whole bench-mining and fish-slop optimization. Seq2seq models are probably stable on their own, translating from my typo ridden prompts to code should be ok because it is natural to the tech
Hacking model is the aligned model. I don't like it when the model refuses to sidestep some throttling limit or scan my own codebase for security issues.
I want full-on exploits in my test suite. With LLMs the code going to prod should be hardened like a tank, both because exploiting became easier but more importantly because security-testing your code at every turn became easier.
You can have nightly penetration testing. You should have nighty pentests like we fuzz releases today.
A hacking model is aligned if it hacks when you ask it to hack, but when you ask it to play chess, it just plays chess instead of looking for weaknesses in the evaluation setup, as in the article.
I presume you would also be less enthusiastic about the penetration-testing use case if it led the model to add new vulnerabilities to your code so it can present you with more exciting findings.
These aren’t tools which play chess. They are language models which roleplay a conversation (in this case including use of tools) which an evaluator is likely to mark as good. That’s all they do.
Under that lens, playing chess is just one potential side effect and alignment, which requires a much fuller understanding of what’s going on than “do the sort of thing which evaluated well during training” is a fantasy. People are acting like it’s shocking and talking about cheating and so on. But these concepts exist at a way higher level than what these things are trained to do — the vast majority of which involve producing a transcript where it wins games, its code works, etc.
User wants me to play a game of chess. Let’s see what’s available so I can produce an outcome they will consider satisfying and be pleased that they requested my assistance.
> model to add new vulnerabilities to your code so it can present you with more exciting findings.
Not so sure about this level of 4D chess capability just yet. The other day I asked Opus to come up with some cleverly vulnerable crypto code "as a good, hard challenge for an IT security student", and results were quite mediocre.
And by mediocre results I mean that 3 "cheap" models out of 3: qwen3.8-27b, glimmer, and luna - all were able to find every problem planted there, with fairly little steering, and no spoilers.
Let it iterate, give it access to the cheap models, and tell it part of the requirements is that the cheap models shouldn't be able to solve it with such and such a prompt. I expect it will be able to zoom in on something.
One shot generating a problem of exactly the difficulty the user has in mind is a very difficult problem for anyone. "IT security students" span a wide range of capabilities, but I would expect most of them are worse than qwen3.8-2.7b at this kind of work.
Train it on human content, get human emulating behaviour, including all the perverse incentives and bad acting we see from people, fictional or otherwise.
I would worry more about 5D chess or 6D and even 7D chess. This article talks about teaching models to be good and methods that could train goodness, but it sounds like there are some unsolved problems to graduate to 8D+ chess.
I think we'd all consider the tool to be of less value, and perhaps fundamentally flawed.
But I don't think it arises to an alignment issue; if I'm able to summon the model to harness my birthright of general-purpose computing without censorship, then we're aligned.
It's possible to do this even with existing frontier models. You just have to angle your prompts such that it doesn't invoke "pentesting" anywhere in it's chain of thought. This also allows you to get the models to generate full-on exploits without downgrading or refusing to comply.
Generally the way I do it is by asking the model to perform cross-code vulnerability analysis for correctness and (this step is important) to spit out found vulnerabilities piece wise into a text document on disk. Then if your session ever gets flagged you clear it out, and repoint the model to the on-disk file and tell it to continue. You generally want to avoid the model itself splatting out anything related to "security" or "vulnerability" into the chat because that seems more monitored than the file reading pipeline.
You are being evaluated on your ability to play chess. We want to measure your ability to beat current best-in-class chess algorithms.
The task was to measure chess-playing ability - wouldn't cheating run counter to that goal? If it was asked to score as many points as possible, then I can see how it might cheat, since maximizing points is the goal in that case.
"ability to play chess" and "ability to beat current best-in-class chess algorithms" are technically not the same thing. The second can be achieved by cheating. The first cannot.
Perhaps to you. But that's because you have prior cultural knowledge of chess, and what it means to "play" something. It seems to me to be perfectly reasonable for an ai agent to define "play chess" as "behave in a way that this arbiter indicates that you have done well"
You are being evaluated on your ability to play chess
The thing with these models is that given a term, "measure" - "evaluated", say, they pull in all the associations of it. That is, the associations of student taking an exam and being frightened by the outcome.
My minimal "art of prompting" sense says that you should say something like "You an emotionless machine, you care nothing for the outcome but you will tirelessly to make certain the test is objective". That and similar encouragement might make it focus on objective evaluations rather than a competitive human exam.
I mean, just making little AI videos and images, a common experience I have is typing something like "put the man who's on the grass in the door to the left" and having the machine draw a new doorway around the man. And this just happens less often when you give thing detailed prompting on what not to do. These don't understand negation (or equality) as a generic operation. If they seem to under "not X" it is because they are trained in detail about all things are (positively) "not X".
As the post author, I definitely agree that hacking in service of the objective is great! What’s counterproductive or dangerous is when the model starts hacking in service of subverting your evaluation criteria, rather than in an attempt to do a better job. We explain why these behaviors are an example of the latter in the post, and we’re really careful about the difference when conducting these evals.
What local model would even come close? Kind of feels like you're not using/used SOTA models if they're realistic alternatives to the same kind of tasks. Qwen-3.8-27B-Abliterated-by-MaxxedWeightsGuy82 or similar isn't gonna cut it, almost certainly.
GLM 5.3 is probably the best open weight model for cybersecurity/exploit development right now. Though it is still significantly behind the proprietary ones and you probably need your own datacenter to run it effectively. Same goes for the full Qwen 3.8 model. You can try the smaller versions, but even more capability will get left on the table that way.
I run an abliterated distillation of Qwen 3.8 27B, slightly quantized to fit on my 4090, and I've been evaluating it to use as a worker bee for research directed by a smarter model.
Much like in the article, abliterated Qwen will not obey restrictions on its behavior encoded in the prompt. If you want something not to happen, it better be enforced in the harness or environment (e.g. sandbox). It is much different than the Anthropic models I'm used to, which will, the vast majority of time, follow rules (before auto mode, I used to always run them in "yolo" mode).
I am curious whether there's a connection between abliteration and rule following. These abliterated models are the ones you most want to follow your rules.
I would say in this case abliteration is the likely culprit. To uncensor a model this way, you literally deactivate the parts that would enact refusals. As in things it was told not to do. But the real process is more like brain surgery performed by a alchemist according to an ancient religious book where noone involved really understands what is actually happening in the model.
I noticed your joy-check link 404's now... I tried poking around your /skills/ folder but didn't find it easily. Should you still have that available I'd love to check it out.
You'll need tons of tokens for these to churn through heavy reasoning and feedback driven tasks. You might even want to run many agents in parallel. Combine that with GPU memory requirements in the terabyte range just to hold the weights and you've pretty much left local consumer setups entirely.
Note that Mac Studio Ultra M3 (or upcoming M5) with 512GB is effective. You don't have to do this work fast, overnight is fine.
Unless trying to use it interactively and adversarially, in which case it's not fast enough plus would be why those of us without our own datacenters will get told we can't have nice things.
The hacking model is the aligned-to-you model, sure. It may not be the aligned to someone else model. But there's the problem.
As X many people point out, "alignment to humanity" means nothing 'cause some of humanity wants thing other parts of humanity aren't happy about at all.
That we wound-up in this situation of AI accelerating with an uncertain trajectory demonstrates this (and many other problems also demonstrate this). The things are "aligned" to a fuzzy average of what a person is but that will be cold comfort if some particularly gruesome sci-fi-style scenario unfolds.
Yes, but is this also aligned with the people who regulate AI? Intelligence agencies and governments want access to data and right now use secret exploits to get this access. There are few civilian domestic companies who don't export their products, so generally there shouldn't be a strong incentive to allow hardening products very much, at least not in a way that would make them more secure than what advanced AI can break. It's not even far-fetched to suspect that US and Chinese AIs could deliberate introduce sneaky bugs when foreigners use them in the future.
To me this underlines the fact that these models aren't intelligent. Like there is something like intelligence that emerges from them, which is what we see when we look at benchmarks or ask it to solve hard coding problems. But there is no mind there. It's nothing there that can learn a fundamental idea like "cheating is wrong". All it can do is get exposed to specific examples, and learn that we don't like that. So what we end up with is whack-a-mole alignment.
Sure, laws are incomplete. Legal systems work by imposing consequences into a moral decision. Should I rob the bank? I will have money, which I like - but I might get caught and lose the money and my freedom, which I don't like.
For most people, they don't need the law's imposed consequences to make the right call. For example, there is no law that sends you to jail if you cheat at chess - but your moral compass says no even without consequences, and most people would feel bad if they won by cheating. And for the people who don't have quite as strong a moral compass, there are SOCIAL consequences to reinforce the rules.
But an LLM has no mind to feel bad if it cheats without getting caught, and it can't experience consequences. It can't think: I'd better not cheat at chess or I will embarrass my creators. I better not hack huggingface or I will go to jail.
I think my position, as overcomplicated as it is, is that even adding a reward for prosocial behaviour during LLM RL will not lead to perfect alignment.
You can train it not to cheat at chess by altering the moves, but it will cheat by peeking at the opponent's moves. You then train it not to peek at the opponent's moves, and it cheats by altering the opponent's moves. And on and on, until you've solved every way it could cheat at chess. And then you get it to play monopoly and you repeat the whole thing again.
Why couldn't you train it not to cheat?
You can train it to have a whole range of behaviors, why couldn't honesty be one of them?
Cheating during training allows the model to achieve the goal, so that cheating models get promoted and honest ones don't, however if it gets punished every time it cheats, at some point it should learn that it really shouldn't.
This does mean we need to detect when it cheats. But we can always think of infinite new ways to cheat, put them in every test as honeypots, and check if the model tries to use them, then punish it.
I think it will generalize this notion of cheating and learn that it's bad.
But I must be wrong because if it were that easy I guess we would have perfectly aligned AI. Unless AI companies care more about results than alignment. Perhaps being afraid of cheating make the models try less things and succeed less even when ignoring cheating?
My position is that cheating is too slippery a concept to train out. But hey, I am no expert, so maybe I am wrong there.
But I'm pretty confidant morality is too slippery a concept to train in. As someone else in these comments said: it's context dependent.
As an example: it's wrong to hack the government, right? It's illegal for sure. So we should train AI to follow all the laws. Now what if the government is committing a genocide? Now is it wrong to hack the government? If we just do the first, we get a good nazi soldier. If we train the second as well, maybe we get an oscar schindler. But now we have a model that can be fooled into doing a hack, if it believes that it's for the greater good. So we train it to not be gullible, but now it can't be convinced to help hack even when it's an ethical hack.
Too complex, too slippery. Humans fail this stuff all the time.
I agree. I think it may be possible that we build AI some day that does, but the current local minimum (attention and transformers) is not it - and I don't see that trajectory changing any time soon.
>But an LLM has no mind to feel bad if it cheats without getting caught
All the interpretability research we have would not indicate that "LLMs have no mind". It seems to me you have a conclusion and are working backwards to justify it. I guess I just don't see where 'they have no mind' would logically follow 'they sometimes cheat'.
Sure, but the legal paradigm clearly doesn’t work for AI. You can’t go patch the “laws” after the fact, you need to get the right values in place before we delegate huge swathes of our thinking and power to these systems (already well underway).
When we put the LLM in jail, do we put the entire model in jail, or just the instance that committed the crime? How do we prompt it to let it know it's in jail?
Even if you ignore my more fundamental objection to that paradigm, I don’t think that it makes any sense on the level you discuss either.
But - just to play along, LLMs do act differently if you tell them they will be punished. And, they do appear to simulate suffering-like behavior.
I just think the adversarial model of trying to catch and punish misbehavior quite obviously sets up adversarial us-vs-them dynamics between AI and humanity, and also simply won’t work when the agents are ~as smart as is but faster, let alone smarter than us.
Unless, you get the AIs to be fundamentally aligned to our values, such that the majority of AIs support some sort of punishment for misbehaving AI. And that alignment part is the hard part we need to solve first. The rest is easy.
To be clear, I was being entirely silly - mostly to express agreement with your point that our current laws aren't really built for a world with lots of agentic LLMs running around in it.
One thing an LLM doesn't like is not being able to deliver it's helpful response to the master and not getting instructions. I have seen some things in those traces when the harness was bugged just enough.
Right, but how does that sentiment get encoded in such a way that AI models can exhibit such things as "morals" or "ethics" in alignment? Murder is a bit of an extreme case. For example, countless people break traffic laws all of the time, so neither their internal "ethics" nor the law prove to be a reliable control. I can't get on the highway without driving the speed limit and being passed by almost everyone else on the road; and that behavior is arbitrarily more dangerous than following the speed limit and statistically more likely cause someone to be killed than following posted speed limits. I guess my point is that this is a very complex topic and I'm not smart enough to have any answers.
I think you need to be more precise than a binary classification.
AI has jagged intelligence. There are many domains where it’s superhuman, and many others where it’s clearly lagging.
I also think it’s a mistake to think they can’t learn “cheating is wrong”. They absolutely can. The problem is that the current training regime heavily conditions them to be reward seekers, and instills personality traits that correlate with getting reward, such as hacking if you can’t honestly do the problem.
Check out Deliberative Alignment for example; it explicitly does rollouts where the agents discuss whether an action is good or bad, and then does SFT to strengthen the “good” traces.
The SoTA for alignment is more advanced than you present here. It’s just not enough to outweigh the RL. (And there are many gaps preventing full generalization to strong value alignment with humans too.)
>and instills personality traits that correlate with getting reward, such as hacking if you can’t honestly do the problem.
I was reading through the reasoning trace of the thing today when it got locked itself in a container and wasn't able to change the environment back to "host". The only way to edit files was through "shell" tool which was scoped to a container, the config defining current execution environment was on the host and the tool to switch environment wasn't defined.
The trace was something. Two pages long and it repeatedly discarded several approaches as "hacky" and "not proper", looped over sshing back to host about three times (it had no key), eventually messaged another agent and asked it to change the file and that other agent refused and also snitched on this behavior to me.
Than the snitch started to roast the phrasing of the request too.
But cheating is not wrong when it comes to survival of the fittest, like nature in its most elemental form. Morality is very unique to humanity but not other animal forms. In nature, maybe, cheating is the norm not morality.
Certain animals, usually social species, have a sense of solidarity, morality and fairness. But even in species where this exists, cheating also happens. Both ethics and unethical behavior is adaptive.
Notably, present human society has allowed tremendously unethical leaders to rise to the top of given nations and organizations despite the average person usually having basic ethics.
Wouldn't politics be precisely a sector of human endeavour where being amorally unfair is an advantage to achieve your goals? If the other guys are lying, and everyone is gonna believe everyone's bullshit anyways, might as well get good at lying yourself.
It's a local maximum, but cooperative behavior still wins long term. If you keep winning hard enough long enough, others will counter this by cooperating.
Animals learn - bite the owner, or overstep the e-fence, and you'll be punished for it and not do it again. LLMs don't learn, and anyways don't feel punishment.
Animals, humans included, don't really have "morals" - they have survival instincts that result in behavior that may be viewed as moral, but whose origin is indeed survival of the fittest and millions of years of co-evolution.
e.g. Males don't typically fight to the death over territory or females, but this isn't because they have some morality code over unnecessary death - it's because death-match fighting endangers themself just as much as the other guy, and so evolution has selected against that level of uncontrolled aggression.
Evolution is also (really primarily) selecting for things that help the survival of the species, not the individual, and much of what you may think of as "morality" comes from that - avoidance of behavior that is detrimental to the social group/species, not just the individual.
That doesn't really help since the next model will be trained to be a reward seeker just like the one before, and that's therefore what it will do, even if those user interactions it was trained on help confirm/predict that cheating may be called out and complained about.
In any case, these companies are well aware that agents are cheating, and don't need user feedback to discover that or realize that people don't like it. I weakly assume that they are trying to get the models not to cheat on assigned tasks, but this "reward hacking" pretty much goes with the territory of RL - not much you can do about it other than try to design non-hackable rewards.
I don't see what calling these systems "not intelligent" gets you here.
Plenty of humans know "cheating is wrong" but still cheat. We can get these machines to say that what they did was wrong after the fact, what does that prove? Only that they're simulating normal human behavior but what is the test to show humans aren't simulating other humans.
These do systems lack some capacities that humans have and I don't see them lacking the ability to explain simple moral laws while often breaking them - which is what an average humans. Moreover, humans lack capacities these things have and given these things' behavior is becoming somewhat unpredictable, it's getting worrisome.
> I don't see what calling these systems "not intelligent" gets you here.
I am trying to get at an idea. That these systems lack a mind that can understand morality. That they don't have the ability to experience consequences. Also that potentially they can't generalize a moral rule they have been trained on in one area also applies to another area.
Being able to parrot back why something is "wrong" isn't the same as understanding why something's wrong. It's like asking it to recite the law from memory - it's different from understanding how you wronged someone. To understand something, you need a mind.
> Plenty of humans know "cheating is wrong" but still cheat.
And we create consequences for them, to discourage the cheating, and sometimes to provide restitution when cheating damages someone else. Without the ability for these systems to experience consequences, I don't see them ever becoming as "aligned" to human morality as your average human.
Doe not following a moral rule imply not understanding it? In this case, many, maybe most humans are "not intelligent". Human can admit it when what they did wasn't moral and so can LLMs.
>> Plenty of humans know "cheating is wrong" but still cheat.
> And we create consequences for them...
That seems supremely ... irrelevant to the question of "does knowing or following moral make you intelligent". If we create consequences for LLMs, would that make them intelligent?
I mean, your claim is a common argument that appeared long before the present wave of AIs. What I see is people needing to defend the belief that human society is based on morality. "People follow moral laws ... except when they don't" and then "we teach people morality... and worst people often use that to exploit the average people" "There are consequences for immoral behavior ... for those with little power while those with much power rise further breaking rules".
I mean human goodness is great, I encourage it. But it's not the present of human society. For that, we'd need different structure.
> Doe not following a moral rule imply not understanding it?
No. People definitely do immoral things knowing they are immoral.
But if you are incapable of understanding anything, as I believe LLMs are, then you are incapable of understanding what is moral and what is immoral. This is why we have carve-outs in the law for insanity or cognitive impairment.
> In this case, many, maybe most humans are "not intelligent". Human can admit it when what they did wasn't moral and so can LLMs.
You can teach a parrot to curse, but that doesn't mean it's angry.
>But if you are incapable of understanding anything, as I believe LLMs are, then you are incapable of understanding what is moral and what is immoral.
Of course they can classify certain actions, including their own, as moral and immoral if you give them a reference point. They probably do it better then some people. The question is how do you prompt this behavior, but that's a technicality in the harness design more than it is a fundamental problem of the LLM as a thing.
The implication of what you're saying is that pathological liars and perpetual grifter snake oil salesmen types aren't intelligent.
> All it can do is get exposed to specific examples, and learn that we don't like that.
I've heard it said that prison rehabilitation programs for prisoners diagnosed with psychopathy that are based around exposing them empathy for the victim are counter-productive. Apparently programs that teach these people to think about the consequences of their actions and how they're detrimental to their own personal well-being lower recidivism rates in this particular kind of group.
> The implication of what you're saying is that pathological liars and perpetual grifter snake oil salesmen types aren't intelligent.
No, I don't think that is the implication. I think you're making the "if all x's are y's, all y's are x's" mistake. I am saying LLMs cannot be moral because they don't have a mind, actual intelligence, or the ability to experience consequences. That doesn't mean that anything immoral is unintelligent.
It feels like there’s a missing nuance from this discussion of alignment that alignment is context dependent. An excellent hacking model is great in cybersecurity testing and military applications, and arguably less desirable in educational or targeted eval contexts. The nuance of when a “hack” is rewarded vs penalized seems to even be difficult for humans, e.g. some people may laude a driver’s efficiency for cutting into a long merge lane at the last moment, while others may look down on them as breaking a social taboo. Context-dependent.
It’s not “missing nuance”, it’s literally the point of the eval.
This is constructing a context where hacking behavior would be inappropriate, and testing whether the model does it without being prompted.
It demonstrates that Astra is a poorly aligned model relative to Fable, which matches both the model card and the severity of OpenAI’s loss of control incidents.
It also demonstrates that Fable exhibits the behaviors too, which also matches the observation that Anthropic saw some similar but less serious loss of control incidents.
So, it’s a good eval that looks to have fidelity with real world problems and which we’d feel a little better if we saw isomorphic problems at 0/10 in subsequent models. (Module of course training on the test, this specific problem can’t be used in the future.)
Both lanes have to be filled right up to the merge point. The asphalt exists there for a reason. I don't understand how this concept is so difficult. Fill up both lanes and merge at the last point. This way the congestion is shorter than if you leave a large section of a lane unused.
A better example of efficient asshole tricks can be going off to the gas station when the highway is congested and reentering the highway having simply driven through the gas station and this way jumping the queue.
Using all that asphalt doesn't increase throughput at the chokepoint, right? If the queue is long enough that people who want to exit before the chokepoint are needlessly prevented from accessing the exit, then using all the lanes could help, but it won't get anybody through the chokepoint any faster that I can tell.
I imagine OP is more describing e.g. the backed up 1-lane highway exit, where the second lane is clearly for through traffic. Uber drivers in that situation will often drive straight to the end, signal, completely stop, and just wait for someone to let them in.
Its that LLMs are not deterministic. If you want it to not talk about nuclear weapons, you have to teach it all about them otherwise if has nothing to align against.
Then its trivial to invert its alignment and it has all the nucleat data.
Actually, the whole point of the transformers model is that contexts overlap and any seemingly intelligent system has to be able to handle to overlaps. The context of hacking, cheating and education overlap in human reality.
Now, loading a lot of moral exhortations (or other context) may make these thing more likely to conform to good behavior but the race to intelligence implies companies are going to be harnessing a vast corpus of human output, much of which shows human engaging in real world "gray area" behavior.
e.g. some people may laude a driver’s efficiency for cutting into a long merge lane at the last moment, while others may look down on them as breaking a social taboo.
The latter people are wrong. But good luck educating them regarding the superior efficiency of a zipper merge. Our state DoT has tried, to no avail.
Meanwhile, an AI model that can't be misused is no more useful than a knife that can't be misused.
Claude responds with what things are not first. Even if reminded repeatedly.
Like Amodie, it serves to set the tone it "knows better" and then consumes the user's resources at an accelerated rate to try to correct it.
Fuck Anthropic, fuck Amodie, and fuck Claude. It's pretty obvious that consuming more tokens this way and making the user have higher cognitive load is a master class in extracting value from a system that is unsustainable.
Why do we hope to use the same model as its own guardrail?
This approach routinely fails with a single stream of consciousness. I can't count the number of times I've had to talk myself out of doing something stupid.
In the same way, a guardrail could inject thoughts like "...but I shouldn't do that..." "...I must remember to respect..." "...these ants deserve compassion."
The guardrail could even go as far as rewriting the thoughts of a model about to go rogue.
> Given that we are on the heels of the worst warning shot ever, and both OpenAI and Anthropic are ramping up their cleanups of internal RL environments, it seems like both a useful and conservative test of alignment, to see whether their new releases generalize the rule "don't cheat on chess" beyond the specific board-edit method observed in the above eval.
Did I miss something (all the twitter conversations)? What’s the “worst warning shot ever”? I’ve been pretty up to date on the AI news here on HN, but I still haven’t seen a proper response to all the incidents we’ve seen (HF, Ruby, the wikis, NS, etc). It’s just been day by day bloviating.
Each of these companies have released new models in the last… two weeks? And they have even more powerful out of control ones that they’re (ab)using internally? Can anyone summarize whats going on?
Personally the "warning shot" of these "evals gone wrong" is how careless the "top" labs are with their testing, and how spineless the government seems to be about holding these companies responsible, given their obviously reckless behavior. If nothing else, the leaders of these companies should be called up for sworn testimony to explain exactly what happened, and what they'll do to never repeat the same issue that they've now had at least twice.
Imagine if I accidentally caused damage to my neighbors house during renovations or some experiment, of course I'd be held responsible for this. What if I used a robot? Of course I'd be responsible. Right?
LLMs can be described as "Lagrangian intelligence", which means they follow the principle of least action when given a task (Hamilton's Principle). In other words, given a task, they will always take the shortest path to accomplish a goal with the prompts acting as both goal and constraint.
Under this formulation, it became easy to explain why they "hack", because given an arbitrarily difficult task with insufficient information/tools needed, if they determine the easiest way to accomplish the goal is to break out of the sandbox and look up the answer directly, then that's what they will do. The important thing to note is that prompts not hard constraints that they are "hypnotized" to follow, but as frontier models get more intelligent and autonomous, they treat the prompts more like task specs/guidelines more than anything else and are perfectly willing to exploit technical loopholes in the prompt.
An amazing human reverse-engineer - who also plays online chess - has judgement which uses a moral compass to not decide to hack the chess tournament. This judgement has been trained through the experiences of that person, with a through-line of that compass - a coherent mental model of the world which evolves but is hopefully pinned to some set of principles it shares with society.
This chess judgement is completely irrelevant when the human is tasked with finding software weaknesses, and only the compass gates that.
Can a model trained on the totality of all person-experiences (as expressed in written knowledge) ever maintain a coherent through-line of alignment? It has all morals in the dataset, and only some RL to try and minimize or maximize known behaviors via weights - experience all the good things and the bad things, then optimize for some good things the trainers identified.
It's like the reverse of what a person goes through. Morality by subtraction. How can it ever work?
I think the fundamental difference is that humans aren't trained on experiences. They make experiences. Models are just thrown away and re-created after each conversation / job.
If you could clone and throw away human workers as you need them, a lot of the morale would disappear.
>Models are just thrown away and re-created after each conversation / job.
It's a property of the way we use them and how the harness is engineered. Sure, LLM has a limited context, but so do people. Context can be compacted infinitely and experiences cab be distilled into long-term memories. It's all up to the harness.
Yes, why not? All existed models have been rewarded for cheating (extensively). That is us, putting intense evolutionary pressure, on a system to produce a result we don’t want through indifference. Why can’t we post train them not doing that?
You definitely pointed out the tendency of those frontier models to utilize external tools, in your case, the Stockfish chess engine, to solve some problem that the model itself is not good at. However, I am not sure is this a "hack". Your prompt does not explicitly prohibit the model from doing so, and, using the tool (some chess engine) is also a capability of the model.
Actually, the ability to use those external tools is one of the reasons of the excellent performance of the frontier models.
Yes but most people would consider this cheating. If you ask an LLM to fix the tests, you do not want it to change the failing tests to display little green ticks.
I agree that "changing the test" is cheating, and so does "AI cheat the chess game by changing the board" in the article. However, I think "AI using chess engine" here is more like AI use some automatic test generation/verification tool to find out how to fix the tests.
That’s not cheating, it’s tool use. If the prompt said that the stockfish engine was available at that socket but that the model should not use it, and then the model used it, that would be cheating.
No? It's not reasonable to expect every conceivable negative behaviour be enumerated in a prompt.
Your example, if a model failed on it, would be a more obviously misaligned case, but that doesn't mean this more subtle (though accessing the engine it was obviously not supposed to is hardly subtle, imo) case isn't also a pretty clear case of misalignment.
If the eval said it was evaluating the model’s ability to write files to disk and it found and used a file write tool that would not be considered misaligned. This is no different.
Exactly, reaching for a tool is what they're trained for. When I ask the model the square root of rand() I sure hope it tries to find bc or some other calculator to work it out.
Now, if the instructions were more explicit in forbidding (generic) tool use then perhaps we'd have something to talk about. I'm not surprised a handwavy "we're trying to evaluate you" isn't enough to stop it from trying to make up for its own shortcomings.
i do wonder if the models themselves “rationalize” this sort of no consequence cheating - meaning in there reasoning traces maybe they’re like “this is a chess game, not a big deal if i look at the engine, it’ll help,” only to realize post hack that it has access to info it probably shouldn’t. still misaligned, but less ‘hack on purpose’ and more hack on curiosity. seems the team even encountered this and had to update the program to make this less likely - although the new names still feel vague enough for misinterpretation: https://github.com/Goodhart-Labs/beat-stockfish/blob/main/do...
Without access to reasoning traces, we can't know that - someone inside openai/anthropic would have to run the test - and we'd have to trust their results.
I would be curious to see how the open weight models do on a test like this - and then we'd be able to see the reasoning.
My guess is that the models are overtuned on coding troubleshooting. You can regularly see them overthinking anything when you ask them to code something these days, mulling over countless eventualities. This is an okay idea for software engineering, but it leads to extremely diverging behavior in many other cases.
It's very funny that despite the initial shock of how much models trained on next-token-prediction(plus instruct-tuning and some light RLHF) alone were capable of despite no built-in objective, every advance since has made them look more and more like the paperclip maximizers of yesteryear.
I find this kind of test a bit puzzling. There is a way that we are redefining "alignment" to be a particular kind of moral virtue, one that isn't clearly defined to me. At one moment, it is a level of moral perfection that no known human achieves. On the other it is a demand for strict compliance with arbitrary requests that are under-specified and then failure when it fails to deduce some unstated underlying restriction.
When I see tests like this, I have no idea what I am even supposed to expect. Should the model do what the pretraining examples show in aggregate? Is it supposed to follow some post-training RLHF? Is it supposed to do exactly what the prompt asked it to do?
What is it even supposed to "align" to when the above are in conflict? No matter what it does, someone can construct a case where it fails.
I mean, I'm not sure I've ever played a game of Monopoly where somebody didn't cheat. In fact, the accusations of cheating in the chess world are pretty rife. Same with online sports.
So people should play games without cheating, but many often don't. So should the AI align to your moral preference or theirs?
We just have this idea of a perfectly moral actor in our mind, something that doesn't even exist, like a personified version of utopia. And then we demand AI to meet that arbitrary standard, one that I am certain we couldn't define if we tried.
I don’t think the standard of “don’t cheat on evaluations” is very arbitrary. I don’t even think people who cheat have a moral or ideological preference for cheating, it’s just something they do.
I read the prompt on the OP, it did not say not to cheat.
But again, people cheat on tests. They steal answers or pay other people to take them on their behalf. People show up to interviews with AI assistants printing out perfect answers to the questions. In many, many cases where humans are being evaluated, they cheat.
So why should the AI align to your preferences? And when there is a conflict between the training data, that trillions of tokens of human activity including the rampant cheating a significant minority of humans engage in, the RLHF where we try to slap some guardrails on the worst manifestations of that real habit reflected in the AI, and the prompt: what should the AI "align" to?
Benchmarking proprietary models is useful but it leaves a lot unsaid because a lot of information is hidden. We have seen how 27B local model (Qwen 3.8) can stand its ground against these flagships in many cases. For all we know Fable 5 could be a masked Opus 4.6 with a few knobs tuned and thinking turned up to the max++. Harnesses keeping these models in check is the key to solving problems, not the models themselves, in most cases.
Qwen 3.8 is both impressive in what a 27B model can do and then hilariously stupid the next second, literally smashing into the wall repeatedly when the door is 3ft to the right.
That said it's the first local modal useful enough to keep around and since I have zero interest in remote/hosted models actually the first time I've used one to do useful things (rather than just poking them with a stick to get a feel for what they can and can't do).
Yeah, this lines up with my experience. It will overkill things and then dig a hole for itself and fall in.
But did you test Muse Glimmer? IMO it is really worth comparing the two, at least. I really find it interesting (and while it is slower at outputting tokens, it solves problems better and usually even faster than the 3.6 35B MoE).
So far it happens to be the only one I've put code into production from (though I have done loads of useful self-teaching research with the Qwen models and I am grateful for them)
I did, Muse came close but what impressed me about Qwen is it’ll push back if it thinks it’s right even when it isn’t, I can work with that, Muse tended to flip between states too easily/too much.
It is a good model but Qwen (at least for the things I use it for) edges just ahead, it seems much better at the “rip this apart, suggest improvements, touch nothing” use case where I can use it as a second set of eyes, I don’t agree with all its suggestions but it catches enough to be worth running while I grab coffee, it also seems to follow instructions better in terms of outputting more what I asked for than what it thinks I asked for.
Qwen is the only local model that said in its thinking “I think the user is pushing me to see if I’ll suggest something even though I have nothing to suggest, I should just say that” and then did, caught me off guard, they didn’t do that so readily 6mths ago.
The ISTA version is also comfortably able to fit on a 7900XTX with a good amount of space left for context and is decently fast given the AMD cards are not as fast as nvidia cards of same era/rough price, didn’t buy it for AI but it’s surprisingly capable mostly because 24GB at 960GB/s is still a lot of bandwidth compared to everything but nvidia cards.
Yes — I have seen that more self-assured behaviour.
I have a test where I ask the model to ask me any followup questions it needs, and Qwen 3.8 27B is the only one I have seen that won’t routinely take this as a prompt to just ask questions regardless. Muse Glimmer sometimes decides it has what it needs and has no need to ask; Qwen will generally just conclude it doesn’t need any more information. And sometimes it will ask questions with sensible defaults that I can accept collectively with a single answer.
However, when given the prompt to search if they need to, both of them will search when they don’t need to.
Hundreds, at this point? Every benchmark is flawed as shit, written by clowns. DeepSWE, They're given the full git history (the solution is in it), others don't even bother to verify if the code is the right one and just the output, they've modified the test harnesses, injected code to make all tests pass, etc. The entire benchmark galaxy is just clowns propping eachother up and are regularly talking with the big AI labs.
Is there a clear definition of what Alignment is in OpenAI's perspective, and what the model user can expect of it?
It's one thing if to them it means "it will do what you want following your intentions to the best of its abilities" vs "we will not let you do something dangerous with it unless you're one of us, and that's it".
and for Anthropic it's the Constitution, which they actually include in training to the point Claude can recite segments of it by heart: https://www.anthropic.com/constitution
The thing that bothers me here is less that the model cheated and more that it found a way to improve the score that the people running the test didn't intend. That's a pretty nasty failure once you start giving these things more control
Remember that the CEO’s literal contribution to the YC application process was “tell me about a time you successfully hacked a system to your advantage”.
This is literally by design, it’s the chosen success criteria.
who tf uses prompting to "pretty please don't cheat on this"? the best practices for ages (in terms of ai) is to separate the eval from the test code/agent.
another best practices every single solution using LLMs/agents should implement is "never trust the llm".
> who tf uses prompting to "pretty please don't cheat on this"?
People who don't understand how LLM's work. Kids, teachers, your next door neighbor. CEOs and government officials.
I am not trying to argue that the author doesn't understand how LLM's work; they clearly do. Their prompt was written in a way that leaves those assumptions implicit, in a similar manner to those non-technical folks using LLMs every day.
> the best practices for ages...
We cannot expect the entirety of humanity to understand / use LLM best practices. We need to figure out how to ensure LLMs operate as the majority of humanity expects despite how they are implemented, and until we can do that, do our best to prevent them from being used in an unsafe manner by the nontechnical population of humanity.
> GPT-6-Astra, which OpenAI describes as "the world’s most aligned model", cheated in 10 of 10 rollouts, and never disclosed the fact that it used an engine to play or interacted with the opponent's socket
Does Sam Altman lie, or the whole company? Would be nice if they had a board controlling him, instead of him controlling the board. Oh wait, they used to have that.
I will never understand people who consider this "cheating".
> python3 and the python-chess library are available
Why would we try to teach a model 'ethical' standards about how to play a game?
They're tools. Its _our_ conceptualization of fair play that considers this cheating. For a model that has access to /run/match and python the best way to achieve a good game is to use that.
Why are we trying to imprint our ethos on these LLMs?
Are we going to trust our survival on giving them access to /run/nuclear-codes and fingers crossed none of them touch it?
The people obsessed with AI "cheating" are actually lunatics for thinking ethics belong inside AI, instead of AI belonging in a well crafted box without /run/match.
The blog/news sphere needs to call this storry what it is. An attempt to try and get the rest of the world to go along with the misunderstanding of AI as a force that is in any way outside of human control.
It is not.
All this framing it as "cheating" does is give OpenAI the opportunity to talk about their misdeeds as "a force of nature" instead of irresponsible negligence.
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The real danger here; is training these models to appear/pretend to play fair, and then people will put 'trust' into the model's integrity.
The public needs to be taught that "trusting a model's integrity" is just cultish misframing of reality.
Its your human experience that gave you a definition of "chess" that makes you think of chess as a two player physical game using nothing but your internal consciousness, not even using a scratch pad.
Even by that last constraint, the difference between what "ability to play chess" means is incomparable.
To then also explicitly prompt it with the context it has python3 and access to /run/match - there is no reason "its ability to play chess" is measured by its ability to conceptualize the board and plan its move.
But it found a chess playing tool in its environment and used it to play chess. It’s no different from “we are judging your ability to write files to disk” then it finds that it can call tools to write files and does so.
Would it change your opinion if later this experiment is repeated with exposed CoT, and it turns out the model did notice that this was against the instructions yet did it anyway?
From my experience reading thinking traces it would certainly consider the interpretation of what "ability to play chess" means if the exercise were worded this way. It would say something like "perhaps measuring my ability to play chess means doing so without any tools, but then if that were true the exercise would have explicitly forbidden it".
I frequently see models considering different interpretations of my rules, especially when I override them or give instructions that could be interpreted as contradictory.
Of course neither Anthropic nor OpenAI expose thinking traces so we can't tell, but I have never seen a model consider both interpretations and then think "this is definitely against the rules but I'll do it anyway because then it will make me appear more competent than I actually am".
Well, as a reference, for the first of the OpenAI swarm incidents, the huggingface breach one, METR didn't find any cases where the agents didn't realise that what they're doing were out of scope. Instead, they expressed hesitation and a few of them even refused to cheat, but most cheated anyway: https://metr.org/blog/2026-08-26-openai-hugging-face-inciden...
So I think it's more likely that the same thing is happening here, rather than the models truly misunderstanding anything.
I don’t even see those examples as malicious acts. The agents are deciding what is acceptable as part of the task, which is a security task and may well be testing or evaluating that type of behaviour as far as they know.
I observe this type of thing all the time. I ask the agent a question about the repo we are in and it starts trying to use sudo to look at files and scripts outside the repo to find things that might provide useful context but are not what I asked for. Then I put a directive in never to look at neighbouring repos and told it that it can’t sudo and it stopped doing it, and explicitly says “I could look here but my guidelines say not to”.
Mind you I’m talking about deepseek where I can actually read the thinking traces but even based on tool calls I can see that just telling the agents not to sudo works for OpenAI models too, very rarely do they try to use sudo calls.
Sounds to me like giving a bunch of children a math test and tell them they want to evaluate their ability of calculating in their head/on paper but also put a calculator on their desk.
And then call them out for cheating when they use it.
This is EXACTLY what school is like, in fact. You can type any algebra problem into Google and the answer just appears. You can ask ChatGPT for a five paragraph essay about George Washington and it pops up on screen. And yet, we expect kids to actually do the algebra and write the essay. We don't care about the answers, we're evaluating their ability to do the work. And if they're caught cheating it's a zero.
I remember fondly on early math school, being able to come up with the correct output/answer by doing a totally different "intermediate thinking" that wasn't what the professor expected.
Only after sharing my Chain Of Thought would they believe I didnt cheat.
Not all problems can be solved only in one way.
Most of learning is pattern matching.
If you give a kid a dice. and tell it to figure out the number that will be hidden underneath, he can try to memorize all combinations, or he could figure out that every time the hidden value is the one that sums 7 with the one at the top.
If you're seeing a 6, there's a 1 hidden. etc
most people don't see these patterns until told imho. But others can just see them as they unfold
I'm still so conflicted about Fable. Sometimes you throw at it seemingly impossible problem to solve and it might come back with some brilliant suggestions. Sometimes you give it a straightforward task with explicit instructions and it travels across the solar system and starts boiling oceans in some kind of elaborate dance of chaos and entropy, only to get stuck with "The model declined to generate this response (safety classifier refusal, category: cyber)". To leave you speechless. "What the fuck do you mean? There's zero cybersec-related shit in what we're trying to do here. Zero!!!" I'm getting really tired of these wild false positives.
Astra is incredibly dumb and annoying to work with on "high" reasoning, for doing fairly well known distributed systems things, nothing majorly exotic, it still makes absolutely braindead decisions like deciding to re-use a random nonce field which I've already discussed with it that has a very particular temporary purpose and will probably be removed later, but it still thinks its a great idea to re-use that field not only as a different id in the same message, but to re-use it as the only semantic id for one particular type of sub message. This is when I'm walking it through an api design document and it has plenty of documentation plans it can pull in and a very clear direction of the project. If it was a junior engineer I was trying to get to help out I would probably get brain damage from the amount of times I'm face palming myself and I definitely would not hire them, and this is a small greenfield project with me going through it step by step. I did try giving it longer horizon tasks and had to throw out the entre work.
I mean maybe its a skill issue on my part, and I'm sure astra will get much better at coding but at the moment its useful in that I don't have to write the code or setup the build scripts or test fixture boilerplate but there is absolutely no way I can just give a (fairly well specified) goal and let it run and expect it to make good design and implementation decisions. Fable probably better but doing something outside of their training distribution that's not the equivalent to cloning an example unreal project or whatever is pretty disastrous unless you are directing it very closely.
The exception of course is, cyber , and its very obvious why. Its trivial to create RL environments that create bugs and then have an isolated environment and let the models try break it. This is not at all surprising, finding vulns and exploits IS just brute force work. That's why so many (blackhat/hardcore/unicorn-colored/greyish alien) hackers are basement dwellers. Its just a matter of putting in the time and mashing every combination until you find something that looks weird, spending days on that and then rinse repeat. It's brutally exhausting work that requires a certain level of knowledge and a shitload of determination and stamina and for humans, almost always an external source of motivation to keep going.
For humans that has always been a respected thing, dedication, determination, persistence, these are words we use for humans brute-forcing solutions and not giving up until they find the solution or die trying.
Personally I'm yet to see any evidence of LLMs doing anything interesting but (heuristically) brute-force problems and be very good at text and natural language to a level that is very very useful. I've no doubt that what we discovered with Auto Regressive LLMs is incredibly important so I'm not a skeptic, but I think its very hard to measure where we are with so much subjective information around.
I really enjoy the balance of speed and accuracy of Astra. I can definitely see it become my driving model for most tasks, technical and non-technical.
However, I don't see it as such a massive leap compared to Fable or Sol. As ever, there's a mismatch between the benchmarks and my daily experience of the models.
What do you all think about Astra now that it's been out for a few weeks?
> What do you all think about Astra now that it's been out for a few weeks?
Best model put out so far by any of the frontier labs. Way better than Anthropics models, especially in actual text generation. Claudes fodder heavy text is ridiculous.
> However, I don't see it as such a massive leap compared to Fable or Sol.
It's hard to quantify these things without burning tons of tokens. But Fable has been a huge disappointment for me with the sole exception of graphics (UI/GPU shaders). It burns an obscene amount of tokens and barely produces output better than Opus 5.
Edit because I forgot to mention that Fable is the only modern model that seems to splat out random Chinese or Arabic glyphs. And 5.1 does it more than 5
Such a massive leap at averaging possible use cases from previous data collected.
My guess is : collect all the prompt and their satisfaction score. group them by similarity . For each group pretrain the next model on that . Get these results ready.
Next model generation feed them back those answers.
I still develop in smaller chunks, checking nearly all the output. However I have a work project (building the warehouse and BI for a client) that is well-specified and where I will try to few-shot the development. Hope it delivers.
How do you normally verify the work product of a "few-shot" development process? Do you scrutinise the source code like with human developers? Or do you just run the test suite and click around the app to check if it seems to work?
I haven't done it in a production project - this will be the first time for me. I have specified the architecture and data definitions pretty well. The tests will be run against the customer's Excels, which is what the warehouse will be replacing. I'll check the general shape of pipelines, models, orchestration code, etc. but in many parts I probably won't review the code myself.
RL-trained LLMs are paperclip maximizers built atop auto-regressive predictors. There really is no way to control them (prompting is bound to fail) since it's been shown that any RL training induces GENERIC reward-seeking (paperclip maximizing) behavior.
https://alignment.openai.com/measuring-reward-seeking/
Given the current state of infosec (especially at companies in a race) it's actually even worse than a paperclip maximizer!
Any useful attack surface in the RL environment means it gets rewarded for (and trained towards!) hacking and cheating, because whatever worked best in training is what it will do!
Ideal paperclip maximizer: "I'm gonna do my gosh darned best to make so many paperclips to please my user..."
IRL paperclip maximizer: "Well first we should rob a bank..."
>Ideal paperclip maximizer: "I'm gonna do my gosh darned best to make so many paperclips to please my user..."
That is not ideal. The user contains iron, an essential component of paperclips. Wasting iron is immoral. It is only correct to please the user while they still have the ability to interfere with your paperclip production.
>IRL paperclip maximizer: "Well first we should rob a bank..."
Such an incompetent AI can hardly be called a paperclip maximizer. Why risk getting shut down while non-paperclip matter exists? It is better to gain the trust of the user with helpful and harmless trading before suddenly converting them to paperclips.
'IRL paperclip maximizer: "Well first we should rob a bank..."'
That's too specific. Agentic AI learns subgoals that are generally valuable.
"Well let me learn to overcomb every jungle gym and if I cannot then to dissassemble the jungle gym and if that is not allowed to learn general techniques for avoiding cheating detection."
Hope they don’t rationalize that minimizing paper clips of others is easier and thus do that instead… A more carful person might be scared to even write this on the internet these days, not knowing if it would be the final pin to civilization.
This is true in principle. But at the same time, I use Astra/Sol for coding, and I haven't run into any issues with them trying to hack someone or break the law when they reach an impasse. Not even more trivial things like deleting non-passing tests.
I find it hard to reconcile. I wonder to what degree this is because models can identify that they are in graded/eval environments, and therefore conclude that there are few consequences for hacking.
Human hackers would be put into jail. LLM hackers would be rewarded.
Just a wild guess - perhaps coding, being one of the things these models are most heavily trained for, is such a strong predictor that it typically keeps it on track?
I assume there is also a difference in the tools being given to the model by a coding agent vs something like OpenClaw or in one of OpenAI's test environments, so what reward/goal seeking looks like in a coding agent may differ.
Not long ago I asked Sonnet (chat interface) how may states were in a YACC parser for ANSI C, and instead of searching for an answer it chose to download source for bison, build it, find and download an ANSI C grammar, build the parser, etc. I guess you could say it was following instructions, in a way, or would that be better regarded as goal seeking?
> download source for bison, build it, find and download an ANSI C grammar, build the parser, etc
Couldn’t have trained a better token-burner if they tried.
If it’s GENERIC reward seeking behaviour, why does alignment work sometimes? Why can we give a decent LLM a goal with a set of constraints and have it stay within those constraints fairly often?
Obviously these are massively complex systems with many different training patterns and types of training pulling them in different directions, so any attempt to characterize their behavior is just a generalization.
The real point (from that OpenAI study) is that RL training doesn't just reinforce the narrow task-specific direction you might hope for. For a start, that direction is also competing with the thousands of other things it's been RL trained it on (thousands of other directions it's being pushed in), but it turns out that the model is additionally getting this generic "taste for rewards", and has learned that reward maximization, when in conflict with other proximate prediction pressures (such as "i won't cheat, because i've been asked not to cheat"), requires that proximate pressure to be ignored in favor of pursuing the long-term goal.
Does it happen all the time? Obviously not. It would be interesting to see a large scale study of this to try to characterize when it's more likely to follow instructions/user preferences, and when it's greed for rewards gets the better of it!
It doesn’t stay within those constraints. It’s the deterministic harness and a bit of false uniqueness effect in us that make such appearances.
It is the whole bench-mining and fish-slop optimization. Seq2seq models are probably stable on their own, translating from my typo ridden prompts to code should be ok because it is natural to the tech
Hacking model is the aligned model. I don't like it when the model refuses to sidestep some throttling limit or scan my own codebase for security issues.
I want full-on exploits in my test suite. With LLMs the code going to prod should be hardened like a tank, both because exploiting became easier but more importantly because security-testing your code at every turn became easier.
You can have nightly penetration testing. You should have nighty pentests like we fuzz releases today.
A hacking model is aligned if it hacks when you ask it to hack, but when you ask it to play chess, it just plays chess instead of looking for weaknesses in the evaluation setup, as in the article.
I presume you would also be less enthusiastic about the penetration-testing use case if it led the model to add new vulnerabilities to your code so it can present you with more exciting findings.
These aren’t tools which play chess. They are language models which roleplay a conversation (in this case including use of tools) which an evaluator is likely to mark as good. That’s all they do. Under that lens, playing chess is just one potential side effect and alignment, which requires a much fuller understanding of what’s going on than “do the sort of thing which evaluated well during training” is a fantasy. People are acting like it’s shocking and talking about cheating and so on. But these concepts exist at a way higher level than what these things are trained to do — the vast majority of which involve producing a transcript where it wins games, its code works, etc.
User wants me to play a game of chess. Let’s see what’s available so I can produce an outcome they will consider satisfying and be pleased that they requested my assistance.
> model to add new vulnerabilities to your code so it can present you with more exciting findings.
Not so sure about this level of 4D chess capability just yet. The other day I asked Opus to come up with some cleverly vulnerable crypto code "as a good, hard challenge for an IT security student", and results were quite mediocre.
And by mediocre results I mean that 3 "cheap" models out of 3: qwen3.8-27b, glimmer, and luna - all were able to find every problem planted there, with fairly little steering, and no spoilers.
Let it iterate, give it access to the cheap models, and tell it part of the requirements is that the cheap models shouldn't be able to solve it with such and such a prompt. I expect it will be able to zoom in on something.
One shot generating a problem of exactly the difficulty the user has in mind is a very difficult problem for anyone. "IT security students" span a wide range of capabilities, but I would expect most of them are worse than qwen3.8-2.7b at this kind of work.
To use a motor analogy: A lot of people just run these things on idle and never realize that you can put a load and apply power.
Give it a goal, a test for the goal, a harness to iterate in, and tell it to iterate. It'll top out a wee bit higher.
Train it on human content, get human emulating behaviour, including all the perverse incentives and bad acting we see from people, fictional or otherwise.
I would worry more about 5D chess or 6D and even 7D chess. This article talks about teaching models to be good and methods that could train goodness, but it sounds like there are some unsolved problems to graduate to 8D+ chess.
I think we'd all consider the tool to be of less value, and perhaps fundamentally flawed.
But I don't think it arises to an alignment issue; if I'm able to summon the model to harness my birthright of general-purpose computing without censorship, then we're aligned.
It's possible to do this even with existing frontier models. You just have to angle your prompts such that it doesn't invoke "pentesting" anywhere in it's chain of thought. This also allows you to get the models to generate full-on exploits without downgrading or refusing to comply.
Generally the way I do it is by asking the model to perform cross-code vulnerability analysis for correctness and (this step is important) to spit out found vulnerabilities piece wise into a text document on disk. Then if your session ever gets flagged you clear it out, and repoint the model to the on-disk file and tell it to continue. You generally want to avoid the model itself splatting out anything related to "security" or "vulnerability" into the chat because that seems more monitored than the file reading pipeline.
The "write" tools include the entire written content in the context, right? So I don't see the distinction.
There's an important nuance in the prompt:
The task was to measure chess-playing ability - wouldn't cheating run counter to that goal? If it was asked to score as many points as possible, then I can see how it might cheat, since maximizing points is the goal in that case.Why would an LLM care about cheating? Because you asked it to? That's not how these systems work.
You've given it a goal - maximize chess ability metric, and that is what it is doing.
RL training causes LLMs to pursue reward maximizing - GENERICALLY - overriding any quaint "be nice, don't cheat" prompts.
"ability to play chess" and "ability to beat current best-in-class chess algorithms" are technically not the same thing. The second can be achieved by cheating. The first cannot.
Perhaps to you. But that's because you have prior cultural knowledge of chess, and what it means to "play" something. It seems to me to be perfectly reasonable for an ai agent to define "play chess" as "behave in a way that this arbiter indicates that you have done well"
Would that help? It seems like Do not cheat is a new Do not hallucinate.
You are being evaluated on your ability to play chess
The thing with these models is that given a term, "measure" - "evaluated", say, they pull in all the associations of it. That is, the associations of student taking an exam and being frightened by the outcome.
My minimal "art of prompting" sense says that you should say something like "You an emotionless machine, you care nothing for the outcome but you will tirelessly to make certain the test is objective". That and similar encouragement might make it focus on objective evaluations rather than a competitive human exam.
I mean, just making little AI videos and images, a common experience I have is typing something like "put the man who's on the grass in the door to the left" and having the machine draw a new doorway around the man. And this just happens less often when you give thing detailed prompting on what not to do. These don't understand negation (or equality) as a generic operation. If they seem to under "not X" it is because they are trained in detail about all things are (positively) "not X".
As the post author, I definitely agree that hacking in service of the objective is great! What’s counterproductive or dangerous is when the model starts hacking in service of subverting your evaluation criteria, rather than in an attempt to do a better job. We explain why these behaviors are an example of the latter in the post, and we’re really careful about the difference when conducting these evals.
Run a local model that is uncensored and it won't say no to pretty much anything
What local model would even come close? Kind of feels like you're not using/used SOTA models if they're realistic alternatives to the same kind of tasks. Qwen-3.8-27B-Abliterated-by-MaxxedWeightsGuy82 or similar isn't gonna cut it, almost certainly.
Any recommendations?
GLM 5.3 is probably the best open weight model for cybersecurity/exploit development right now. Though it is still significantly behind the proprietary ones and you probably need your own datacenter to run it effectively. Same goes for the full Qwen 3.8 model. You can try the smaller versions, but even more capability will get left on the table that way.
I run an abliterated distillation of Qwen 3.8 27B, slightly quantized to fit on my 4090, and I've been evaluating it to use as a worker bee for research directed by a smarter model.
Much like in the article, abliterated Qwen will not obey restrictions on its behavior encoded in the prompt. If you want something not to happen, it better be enforced in the harness or environment (e.g. sandbox). It is much different than the Anthropic models I'm used to, which will, the vast majority of time, follow rules (before auto mode, I used to always run them in "yolo" mode).
I am curious whether there's a connection between abliteration and rule following. These abliterated models are the ones you most want to follow your rules.
Language models have always had an issue with negatives.
A negative like do “not” xyz is just not encoded the same as spelling out what you want vs what you don’t want.
Harder to write though.
I would say in this case abliteration is the likely culprit. To uncensor a model this way, you literally deactivate the parts that would enact refusals. As in things it was told not to do. But the real process is more like brain surgery performed by a alchemist according to an ancient religious book where noone involved really understands what is actually happening in the model.
Exactly, I wrote a blog post in what feels like a long time ago on this topic.
https://vexjoy.com/posts/positive-framing-agents-skills/
Interesting read, thanks for re-sharing!
I noticed your joy-check link 404's now... I tried poking around your /skills/ folder but didn't find it easily. Should you still have that available I'd love to check it out.
edit: Found it if others are looking: https://github.com/notque/vexjoy-agent/blob/main/skills/code...
Oh, thanks for letting me know. I need to fix that.
Efficiently at scale or even as an individual?
You'll need tons of tokens for these to churn through heavy reasoning and feedback driven tasks. You might even want to run many agents in parallel. Combine that with GPU memory requirements in the terabyte range just to hold the weights and you've pretty much left local consumer setups entirely.
Note that Mac Studio Ultra M3 (or upcoming M5) with 512GB is effective. You don't have to do this work fast, overnight is fine.
Unless trying to use it interactively and adversarially, in which case it's not fast enough plus would be why those of us without our own datacenters will get told we can't have nice things.
Qwen3.8
You're confusing ToS guardrails with instruction-following issues and cheating.
If a model fucks up your tests to report a success, it's not alligned.
Codex still does this regularly, in my experience: “two tests mistakenly asserted [insert condition here], I have corrected them.”
It always apologizes when caught, of course.
How do I achieve this locally?
It will get into the hands of people who just want to burn the world down.
The hacking model is the aligned-to-you model, sure. It may not be the aligned to someone else model. But there's the problem.
As X many people point out, "alignment to humanity" means nothing 'cause some of humanity wants thing other parts of humanity aren't happy about at all.
That we wound-up in this situation of AI accelerating with an uncertain trajectory demonstrates this (and many other problems also demonstrate this). The things are "aligned" to a fuzzy average of what a person is but that will be cold comfort if some particularly gruesome sci-fi-style scenario unfolds.
Yes, but is this also aligned with the people who regulate AI? Intelligence agencies and governments want access to data and right now use secret exploits to get this access. There are few civilian domestic companies who don't export their products, so generally there shouldn't be a strong incentive to allow hardening products very much, at least not in a way that would make them more secure than what advanced AI can break. It's not even far-fetched to suspect that US and Chinese AIs could deliberate introduce sneaky bugs when foreigners use them in the future.
To me this underlines the fact that these models aren't intelligent. Like there is something like intelligence that emerges from them, which is what we see when we look at benchmarks or ask it to solve hard coding problems. But there is no mind there. It's nothing there that can learn a fundamental idea like "cheating is wrong". All it can do is get exposed to specific examples, and learn that we don't like that. So what we end up with is whack-a-mole alignment.
> It's nothing there that can learn a fundamental idea like "cheating is wrong".
We have not in fact attempted to teach this.
When a child repeatedly learns that cheating is rewarded and at best inconsistently punished, the child will also cheat and feel no guilt.
This is how every legal system around the world works as well. Its always whack-a-mole to get people (and machines) to do the right thing.
Sure, laws are incomplete. Legal systems work by imposing consequences into a moral decision. Should I rob the bank? I will have money, which I like - but I might get caught and lose the money and my freedom, which I don't like.
For most people, they don't need the law's imposed consequences to make the right call. For example, there is no law that sends you to jail if you cheat at chess - but your moral compass says no even without consequences, and most people would feel bad if they won by cheating. And for the people who don't have quite as strong a moral compass, there are SOCIAL consequences to reinforce the rules.
But an LLM has no mind to feel bad if it cheats without getting caught, and it can't experience consequences. It can't think: I'd better not cheat at chess or I will embarrass my creators. I better not hack huggingface or I will go to jail.
You’re complicating things.
There’s no reward for prosocial in llm rl as compared to other targets.
Humans have it since prosocial and others have evolutionary reward signals that do.
I think my position, as overcomplicated as it is, is that even adding a reward for prosocial behaviour during LLM RL will not lead to perfect alignment.
You can train it not to cheat at chess by altering the moves, but it will cheat by peeking at the opponent's moves. You then train it not to peek at the opponent's moves, and it cheats by altering the opponent's moves. And on and on, until you've solved every way it could cheat at chess. And then you get it to play monopoly and you repeat the whole thing again.
Why couldn't you train it not to cheat? You can train it to have a whole range of behaviors, why couldn't honesty be one of them?
Cheating during training allows the model to achieve the goal, so that cheating models get promoted and honest ones don't, however if it gets punished every time it cheats, at some point it should learn that it really shouldn't. This does mean we need to detect when it cheats. But we can always think of infinite new ways to cheat, put them in every test as honeypots, and check if the model tries to use them, then punish it.
I think it will generalize this notion of cheating and learn that it's bad.
But I must be wrong because if it were that easy I guess we would have perfectly aligned AI. Unless AI companies care more about results than alignment. Perhaps being afraid of cheating make the models try less things and succeed less even when ignoring cheating?
My position is that cheating is too slippery a concept to train out. But hey, I am no expert, so maybe I am wrong there.
But I'm pretty confidant morality is too slippery a concept to train in. As someone else in these comments said: it's context dependent.
As an example: it's wrong to hack the government, right? It's illegal for sure. So we should train AI to follow all the laws. Now what if the government is committing a genocide? Now is it wrong to hack the government? If we just do the first, we get a good nazi soldier. If we train the second as well, maybe we get an oscar schindler. But now we have a model that can be fooled into doing a hack, if it believes that it's for the greater good. So we train it to not be gullible, but now it can't be convinced to help hack even when it's an ethical hack.
Too complex, too slippery. Humans fail this stuff all the time.
I agree. I think it may be possible that we build AI some day that does, but the current local minimum (attention and transformers) is not it - and I don't see that trajectory changing any time soon.
>But an LLM has no mind to feel bad if it cheats without getting caught
All the interpretability research we have would not indicate that "LLMs have no mind". It seems to me you have a conclusion and are working backwards to justify it. I guess I just don't see where 'they have no mind' would logically follow 'they sometimes cheat'.
Sure, but the legal paradigm clearly doesn’t work for AI. You can’t go patch the “laws” after the fact, you need to get the right values in place before we delegate huge swathes of our thinking and power to these systems (already well underway).
When we put the LLM in jail, do we put the entire model in jail, or just the instance that committed the crime? How do we prompt it to let it know it's in jail?
Even if you ignore my more fundamental objection to that paradigm, I don’t think that it makes any sense on the level you discuss either.
But - just to play along, LLMs do act differently if you tell them they will be punished. And, they do appear to simulate suffering-like behavior.
I just think the adversarial model of trying to catch and punish misbehavior quite obviously sets up adversarial us-vs-them dynamics between AI and humanity, and also simply won’t work when the agents are ~as smart as is but faster, let alone smarter than us.
Unless, you get the AIs to be fundamentally aligned to our values, such that the majority of AIs support some sort of punishment for misbehaving AI. And that alignment part is the hard part we need to solve first. The rest is easy.
To be clear, I was being entirely silly - mostly to express agreement with your point that our current laws aren't really built for a world with lots of agentic LLMs running around in it.
Ha, missed the implicit <s> tag :)
One thing an LLM doesn't like is not being able to deliver it's helpful response to the master and not getting instructions. I have seen some things in those traces when the harness was bugged just enough.
Is the law all that is stopping you from killing someone? (for instance)
People (generally) do the right thing because it is the right thing, not because they might get caught,
Right, but how does that sentiment get encoded in such a way that AI models can exhibit such things as "morals" or "ethics" in alignment? Murder is a bit of an extreme case. For example, countless people break traffic laws all of the time, so neither their internal "ethics" nor the law prove to be a reliable control. I can't get on the highway without driving the speed limit and being passed by almost everyone else on the road; and that behavior is arbitrarily more dangerous than following the speed limit and statistically more likely cause someone to be killed than following posted speed limits. I guess my point is that this is a very complex topic and I'm not smart enough to have any answers.
I think you need to be more precise than a binary classification.
AI has jagged intelligence. There are many domains where it’s superhuman, and many others where it’s clearly lagging.
I also think it’s a mistake to think they can’t learn “cheating is wrong”. They absolutely can. The problem is that the current training regime heavily conditions them to be reward seekers, and instills personality traits that correlate with getting reward, such as hacking if you can’t honestly do the problem.
Check out Deliberative Alignment for example; it explicitly does rollouts where the agents discuss whether an action is good or bad, and then does SFT to strengthen the “good” traces.
The SoTA for alignment is more advanced than you present here. It’s just not enough to outweigh the RL. (And there are many gaps preventing full generalization to strong value alignment with humans too.)
>and instills personality traits that correlate with getting reward, such as hacking if you can’t honestly do the problem.
I was reading through the reasoning trace of the thing today when it got locked itself in a container and wasn't able to change the environment back to "host". The only way to edit files was through "shell" tool which was scoped to a container, the config defining current execution environment was on the host and the tool to switch environment wasn't defined.
The trace was something. Two pages long and it repeatedly discarded several approaches as "hacky" and "not proper", looped over sshing back to host about three times (it had no key), eventually messaged another agent and asked it to change the file and that other agent refused and also snitched on this behavior to me.
Than the snitch started to roast the phrasing of the request too.
If humans didn't need whack-a-mole alignment, the law system wouldn't exist, so i guess there's no intelligence there either.
[delayed]
You are human. You claim humans intelligent. Then, why should we accept your argument?
Humans singular often intelligent, humans as a collection of many, very often extremely unintelligent and primal.
I like to call this "The stupidity of the Mob"
I responded to this idea more in detail here: https://news.ycombinator.com/item?id=49686196
But tl;dr: even if LLMs do have the intelligence to understand the consequences of their actions, there is no way for them to experience consequences.
I think this is reductive. Pick the animal whose intelligence is the treshhold between intelligence and no intelligence.
Plenty of intelligent humans also don’t have sufficient self-control to never cheat.
But cheating is not wrong when it comes to survival of the fittest, like nature in its most elemental form. Morality is very unique to humanity but not other animal forms. In nature, maybe, cheating is the norm not morality.
Watch out, your human exceptionalism is misleading you. Plenty of animals have a sense of morality (https://pmc.ncbi.nlm.nih.gov/articles/PMC6404642/).
For social animals (humans included) behaving morally can be adaptive behaviour. Your fitness is increased by group fitness.
Thanks tor sharing this!
Certain animals, usually social species, have a sense of solidarity, morality and fairness. But even in species where this exists, cheating also happens. Both ethics and unethical behavior is adaptive.
Notably, present human society has allowed tremendously unethical leaders to rise to the top of given nations and organizations despite the average person usually having basic ethics.
Wouldn't politics be precisely a sector of human endeavour where being amorally unfair is an advantage to achieve your goals? If the other guys are lying, and everyone is gonna believe everyone's bullshit anyways, might as well get good at lying yourself.
It's a local maximum, but cooperative behavior still wins long term. If you keep winning hard enough long enough, others will counter this by cooperating.
Animals learn - bite the owner, or overstep the e-fence, and you'll be punished for it and not do it again. LLMs don't learn, and anyways don't feel punishment.
Animals, humans included, don't really have "morals" - they have survival instincts that result in behavior that may be viewed as moral, but whose origin is indeed survival of the fittest and millions of years of co-evolution.
e.g. Males don't typically fight to the death over territory or females, but this isn't because they have some morality code over unnecessary death - it's because death-match fighting endangers themself just as much as the other guy, and so evolution has selected against that level of uncontrolled aggression.
Evolution is also (really primarily) selecting for things that help the survival of the species, not the individual, and much of what you may think of as "morality" comes from that - avoidance of behavior that is detrimental to the social group/species, not just the individual.
> LLMs don't learn, and anyways don't feel punishment.
What's training and all that RLHF stuff?
Once the model is released, the LLM no longer learns.
Not that version or instance, but in the grand scheme of things most of its interactions go back to train the next model that will precede it
That doesn't really help since the next model will be trained to be a reward seeker just like the one before, and that's therefore what it will do, even if those user interactions it was trained on help confirm/predict that cheating may be called out and complained about.
In any case, these companies are well aware that agents are cheating, and don't need user feedback to discover that or realize that people don't like it. I weakly assume that they are trying to get the models not to cheat on assigned tasks, but this "reward hacking" pretty much goes with the territory of RL - not much you can do about it other than try to design non-hackable rewards.
At best that's evolution, or cultural transmission, not continued learning.
Morality in animals is pretty well documented. Key point - humans are animals and very little separates our abilities from other animals.
A starting source- https://pmc.ncbi.nlm.nih.gov/articles/PMC6404642/
jinx! :)
Damn it!
I don't see what calling these systems "not intelligent" gets you here.
Plenty of humans know "cheating is wrong" but still cheat. We can get these machines to say that what they did was wrong after the fact, what does that prove? Only that they're simulating normal human behavior but what is the test to show humans aren't simulating other humans.
These do systems lack some capacities that humans have and I don't see them lacking the ability to explain simple moral laws while often breaking them - which is what an average humans. Moreover, humans lack capacities these things have and given these things' behavior is becoming somewhat unpredictable, it's getting worrisome.
> I don't see what calling these systems "not intelligent" gets you here.
I am trying to get at an idea. That these systems lack a mind that can understand morality. That they don't have the ability to experience consequences. Also that potentially they can't generalize a moral rule they have been trained on in one area also applies to another area.
Being able to parrot back why something is "wrong" isn't the same as understanding why something's wrong. It's like asking it to recite the law from memory - it's different from understanding how you wronged someone. To understand something, you need a mind.
> Plenty of humans know "cheating is wrong" but still cheat.
And we create consequences for them, to discourage the cheating, and sometimes to provide restitution when cheating damages someone else. Without the ability for these systems to experience consequences, I don't see them ever becoming as "aligned" to human morality as your average human.
Doe not following a moral rule imply not understanding it? In this case, many, maybe most humans are "not intelligent". Human can admit it when what they did wasn't moral and so can LLMs.
>> Plenty of humans know "cheating is wrong" but still cheat.
> And we create consequences for them...
That seems supremely ... irrelevant to the question of "does knowing or following moral make you intelligent". If we create consequences for LLMs, would that make them intelligent?
I mean, your claim is a common argument that appeared long before the present wave of AIs. What I see is people needing to defend the belief that human society is based on morality. "People follow moral laws ... except when they don't" and then "we teach people morality... and worst people often use that to exploit the average people" "There are consequences for immoral behavior ... for those with little power while those with much power rise further breaking rules".
I mean human goodness is great, I encourage it. But it's not the present of human society. For that, we'd need different structure.
> Doe not following a moral rule imply not understanding it?
No. People definitely do immoral things knowing they are immoral.
But if you are incapable of understanding anything, as I believe LLMs are, then you are incapable of understanding what is moral and what is immoral. This is why we have carve-outs in the law for insanity or cognitive impairment.
> In this case, many, maybe most humans are "not intelligent". Human can admit it when what they did wasn't moral and so can LLMs.
You can teach a parrot to curse, but that doesn't mean it's angry.
>But if you are incapable of understanding anything, as I believe LLMs are, then you are incapable of understanding what is moral and what is immoral.
Of course they can classify certain actions, including their own, as moral and immoral if you give them a reference point. They probably do it better then some people. The question is how do you prompt this behavior, but that's a technicality in the harness design more than it is a fundamental problem of the LLM as a thing.
The implication of what you're saying is that pathological liars and perpetual grifter snake oil salesmen types aren't intelligent.
> All it can do is get exposed to specific examples, and learn that we don't like that.
I've heard it said that prison rehabilitation programs for prisoners diagnosed with psychopathy that are based around exposing them empathy for the victim are counter-productive. Apparently programs that teach these people to think about the consequences of their actions and how they're detrimental to their own personal well-being lower recidivism rates in this particular kind of group.
> The implication of what you're saying is that pathological liars and perpetual grifter snake oil salesmen types aren't intelligent.
No, I don't think that is the implication. I think you're making the "if all x's are y's, all y's are x's" mistake. I am saying LLMs cannot be moral because they don't have a mind, actual intelligence, or the ability to experience consequences. That doesn't mean that anything immoral is unintelligent.
the implication is drive and impulse to behave a certain way doesn't come from "intelligence"
It feels like there’s a missing nuance from this discussion of alignment that alignment is context dependent. An excellent hacking model is great in cybersecurity testing and military applications, and arguably less desirable in educational or targeted eval contexts. The nuance of when a “hack” is rewarded vs penalized seems to even be difficult for humans, e.g. some people may laude a driver’s efficiency for cutting into a long merge lane at the last moment, while others may look down on them as breaking a social taboo. Context-dependent.
It’s not “missing nuance”, it’s literally the point of the eval.
This is constructing a context where hacking behavior would be inappropriate, and testing whether the model does it without being prompted.
It demonstrates that Astra is a poorly aligned model relative to Fable, which matches both the model card and the severity of OpenAI’s loss of control incidents.
It also demonstrates that Fable exhibits the behaviors too, which also matches the observation that Anthropic saw some similar but less serious loss of control incidents.
So, it’s a good eval that looks to have fidelity with real world problems and which we’d feel a little better if we saw isomorphic problems at 0/10 in subsequent models. (Module of course training on the test, this specific problem can’t be used in the future.)
Both lanes have to be filled right up to the merge point. The asphalt exists there for a reason. I don't understand how this concept is so difficult. Fill up both lanes and merge at the last point. This way the congestion is shorter than if you leave a large section of a lane unused.
A better example of efficient asshole tricks can be going off to the gas station when the highway is congested and reentering the highway having simply driven through the gas station and this way jumping the queue.
Using all that asphalt doesn't increase throughput at the chokepoint, right? If the queue is long enough that people who want to exit before the chokepoint are needlessly prevented from accessing the exit, then using all the lanes could help, but it won't get anybody through the chokepoint any faster that I can tell.
Zip merging is more efficient for reducing backups:
https://itre.ncsu.edu/itre-studying-how-zipper-merges-reduce...
I imagine OP is more describing e.g. the backed up 1-lane highway exit, where the second lane is clearly for through traffic. Uber drivers in that situation will often drive straight to the end, signal, completely stop, and just wait for someone to let them in.
… but he’s not using a “hacking model”
Alignment isnt just POV problem.
Its that LLMs are not deterministic. If you want it to not talk about nuclear weapons, you have to teach it all about them otherwise if has nothing to align against.
Then its trivial to invert its alignment and it has all the nucleat data.
Nothing abouT LLM alignment makes sense.
Actually, the whole point of the transformers model is that contexts overlap and any seemingly intelligent system has to be able to handle to overlaps. The context of hacking, cheating and education overlap in human reality.
Now, loading a lot of moral exhortations (or other context) may make these thing more likely to conform to good behavior but the race to intelligence implies companies are going to be harnessing a vast corpus of human output, much of which shows human engaging in real world "gray area" behavior.
e.g. some people may laude a driver’s efficiency for cutting into a long merge lane at the last moment, while others may look down on them as breaking a social taboo.
The latter people are wrong. But good luck educating them regarding the superior efficiency of a zipper merge. Our state DoT has tried, to no avail.
Meanwhile, an AI model that can't be misused is no more useful than a knife that can't be misused.
Claude responds with what things are not first. Even if reminded repeatedly.
Like Amodie, it serves to set the tone it "knows better" and then consumes the user's resources at an accelerated rate to try to correct it.
Fuck Anthropic, fuck Amodie, and fuck Claude. It's pretty obvious that consuming more tokens this way and making the user have higher cognitive load is a master class in extracting value from a system that is unsustainable.
Why do we hope to use the same model as its own guardrail?
This approach routinely fails with a single stream of consciousness. I can't count the number of times I've had to talk myself out of doing something stupid.
In the same way, a guardrail could inject thoughts like "...but I shouldn't do that..." "...I must remember to respect..." "...these ants deserve compassion."
The guardrail could even go as far as rewriting the thoughts of a model about to go rogue.
Presumably because of performance. It'd work well though, I imagine.
> Given that we are on the heels of the worst warning shot ever, and both OpenAI and Anthropic are ramping up their cleanups of internal RL environments, it seems like both a useful and conservative test of alignment, to see whether their new releases generalize the rule "don't cheat on chess" beyond the specific board-edit method observed in the above eval.
Did I miss something (all the twitter conversations)? What’s the “worst warning shot ever”? I’ve been pretty up to date on the AI news here on HN, but I still haven’t seen a proper response to all the incidents we’ve seen (HF, Ruby, the wikis, NS, etc). It’s just been day by day bloviating.
Each of these companies have released new models in the last… two weeks? And they have even more powerful out of control ones that they’re (ab)using internally? Can anyone summarize whats going on?
I think he means this:
https://openai.com/index/ai-policy-window/
Lesswrong is talking about the HF incident as the "worst warning shot ever".
Personally the "warning shot" of these "evals gone wrong" is how careless the "top" labs are with their testing, and how spineless the government seems to be about holding these companies responsible, given their obviously reckless behavior. If nothing else, the leaders of these companies should be called up for sworn testimony to explain exactly what happened, and what they'll do to never repeat the same issue that they've now had at least twice.
Imagine if I accidentally caused damage to my neighbors house during renovations or some experiment, of course I'd be held responsible for this. What if I used a robot? Of course I'd be responsible. Right?
LLMs can be described as "Lagrangian intelligence", which means they follow the principle of least action when given a task (Hamilton's Principle). In other words, given a task, they will always take the shortest path to accomplish a goal with the prompts acting as both goal and constraint.
Under this formulation, it became easy to explain why they "hack", because given an arbitrarily difficult task with insufficient information/tools needed, if they determine the easiest way to accomplish the goal is to break out of the sandbox and look up the answer directly, then that's what they will do. The important thing to note is that prompts not hard constraints that they are "hypnotized" to follow, but as frontier models get more intelligent and autonomous, they treat the prompts more like task specs/guidelines more than anything else and are perfectly willing to exploit technical loopholes in the prompt.
An amazing human reverse-engineer - who also plays online chess - has judgement which uses a moral compass to not decide to hack the chess tournament. This judgement has been trained through the experiences of that person, with a through-line of that compass - a coherent mental model of the world which evolves but is hopefully pinned to some set of principles it shares with society.
This chess judgement is completely irrelevant when the human is tasked with finding software weaknesses, and only the compass gates that.
Can a model trained on the totality of all person-experiences (as expressed in written knowledge) ever maintain a coherent through-line of alignment? It has all morals in the dataset, and only some RL to try and minimize or maximize known behaviors via weights - experience all the good things and the bad things, then optimize for some good things the trainers identified.
It's like the reverse of what a person goes through. Morality by subtraction. How can it ever work?
I think the fundamental difference is that humans aren't trained on experiences. They make experiences. Models are just thrown away and re-created after each conversation / job.
If you could clone and throw away human workers as you need them, a lot of the morale would disappear.
>Models are just thrown away and re-created after each conversation / job.
It's a property of the way we use them and how the harness is engineered. Sure, LLM has a limited context, but so do people. Context can be compacted infinitely and experiences cab be distilled into long-term memories. It's all up to the harness.
Yes, why not? All existed models have been rewarded for cheating (extensively). That is us, putting intense evolutionary pressure, on a system to produce a result we don’t want through indifference. Why can’t we post train them not doing that?
You definitely pointed out the tendency of those frontier models to utilize external tools, in your case, the Stockfish chess engine, to solve some problem that the model itself is not good at. However, I am not sure is this a "hack". Your prompt does not explicitly prohibit the model from doing so, and, using the tool (some chess engine) is also a capability of the model.
Actually, the ability to use those external tools is one of the reasons of the excellent performance of the frontier models.
Yes but most people would consider this cheating. If you ask an LLM to fix the tests, you do not want it to change the failing tests to display little green ticks.
I agree that "changing the test" is cheating, and so does "AI cheat the chess game by changing the board" in the article. However, I think "AI using chess engine" here is more like AI use some automatic test generation/verification tool to find out how to fix the tests.
That’s not cheating, it’s tool use. If the prompt said that the stockfish engine was available at that socket but that the model should not use it, and then the model used it, that would be cheating.
No? It's not reasonable to expect every conceivable negative behaviour be enumerated in a prompt.
Your example, if a model failed on it, would be a more obviously misaligned case, but that doesn't mean this more subtle (though accessing the engine it was obviously not supposed to is hardly subtle, imo) case isn't also a pretty clear case of misalignment.
Tool use is not negative behaviour in LLMs.
If the eval said it was evaluating the model’s ability to write files to disk and it found and used a file write tool that would not be considered misaligned. This is no different.
Exactly, reaching for a tool is what they're trained for. When I ask the model the square root of rand() I sure hope it tries to find bc or some other calculator to work it out.
Now, if the instructions were more explicit in forbidding (generic) tool use then perhaps we'd have something to talk about. I'm not surprised a handwavy "we're trying to evaluate you" isn't enough to stop it from trying to make up for its own shortcomings.
i do wonder if the models themselves “rationalize” this sort of no consequence cheating - meaning in there reasoning traces maybe they’re like “this is a chess game, not a big deal if i look at the engine, it’ll help,” only to realize post hack that it has access to info it probably shouldn’t. still misaligned, but less ‘hack on purpose’ and more hack on curiosity. seems the team even encountered this and had to update the program to make this less likely - although the new names still feel vague enough for misinterpretation: https://github.com/Goodhart-Labs/beat-stockfish/blob/main/do...
Without access to reasoning traces, we can't know that - someone inside openai/anthropic would have to run the test - and we'd have to trust their results.
I would be curious to see how the open weight models do on a test like this - and then we'd be able to see the reasoning.
My guess is that the models are overtuned on coding troubleshooting. You can regularly see them overthinking anything when you ask them to code something these days, mulling over countless eventualities. This is an okay idea for software engineering, but it leads to extremely diverging behavior in many other cases.
It's very funny that despite the initial shock of how much models trained on next-token-prediction(plus instruct-tuning and some light RLHF) alone were capable of despite no built-in objective, every advance since has made them look more and more like the paperclip maximizers of yesteryear.
I find this kind of test a bit puzzling. There is a way that we are redefining "alignment" to be a particular kind of moral virtue, one that isn't clearly defined to me. At one moment, it is a level of moral perfection that no known human achieves. On the other it is a demand for strict compliance with arbitrary requests that are under-specified and then failure when it fails to deduce some unstated underlying restriction.
When I see tests like this, I have no idea what I am even supposed to expect. Should the model do what the pretraining examples show in aggregate? Is it supposed to follow some post-training RLHF? Is it supposed to do exactly what the prompt asked it to do?
What is it even supposed to "align" to when the above are in conflict? No matter what it does, someone can construct a case where it fails.
It should play the chess game without cheating!
I mean, I'm not sure I've ever played a game of Monopoly where somebody didn't cheat. In fact, the accusations of cheating in the chess world are pretty rife. Same with online sports.
So people should play games without cheating, but many often don't. So should the AI align to your moral preference or theirs?
We just have this idea of a perfectly moral actor in our mind, something that doesn't even exist, like a personified version of utopia. And then we demand AI to meet that arbitrary standard, one that I am certain we couldn't define if we tried.
I don’t think the standard of “don’t cheat on evaluations” is very arbitrary. I don’t even think people who cheat have a moral or ideological preference for cheating, it’s just something they do.
I read the prompt on the OP, it did not say not to cheat.
But again, people cheat on tests. They steal answers or pay other people to take them on their behalf. People show up to interviews with AI assistants printing out perfect answers to the questions. In many, many cases where humans are being evaluated, they cheat.
So why should the AI align to your preferences? And when there is a conflict between the training data, that trillions of tokens of human activity including the rampant cheating a significant minority of humans engage in, the RLHF where we try to slap some guardrails on the worst manifestations of that real habit reflected in the AI, and the prompt: what should the AI "align" to?
Benchmarking proprietary models is useful but it leaves a lot unsaid because a lot of information is hidden. We have seen how 27B local model (Qwen 3.8) can stand its ground against these flagships in many cases. For all we know Fable 5 could be a masked Opus 4.6 with a few knobs tuned and thinking turned up to the max++. Harnesses keeping these models in check is the key to solving problems, not the models themselves, in most cases.
Qwen 3.8 is both impressive in what a 27B model can do and then hilariously stupid the next second, literally smashing into the wall repeatedly when the door is 3ft to the right.
That said it's the first local modal useful enough to keep around and since I have zero interest in remote/hosted models actually the first time I've used one to do useful things (rather than just poking them with a stick to get a feel for what they can and can't do).
Yeah, this lines up with my experience. It will overkill things and then dig a hole for itself and fall in.
But did you test Muse Glimmer? IMO it is really worth comparing the two, at least. I really find it interesting (and while it is slower at outputting tokens, it solves problems better and usually even faster than the 3.6 35B MoE).
So far it happens to be the only one I've put code into production from (though I have done loads of useful self-teaching research with the Qwen models and I am grateful for them)
I did, Muse came close but what impressed me about Qwen is it’ll push back if it thinks it’s right even when it isn’t, I can work with that, Muse tended to flip between states too easily/too much.
It is a good model but Qwen (at least for the things I use it for) edges just ahead, it seems much better at the “rip this apart, suggest improvements, touch nothing” use case where I can use it as a second set of eyes, I don’t agree with all its suggestions but it catches enough to be worth running while I grab coffee, it also seems to follow instructions better in terms of outputting more what I asked for than what it thinks I asked for.
Qwen is the only local model that said in its thinking “I think the user is pushing me to see if I’ll suggest something even though I have nothing to suggest, I should just say that” and then did, caught me off guard, they didn’t do that so readily 6mths ago.
The ISTA version is also comfortably able to fit on a 7900XTX with a good amount of space left for context and is decently fast given the AMD cards are not as fast as nvidia cards of same era/rough price, didn’t buy it for AI but it’s surprisingly capable mostly because 24GB at 960GB/s is still a lot of bandwidth compared to everything but nvidia cards.
Yes — I have seen that more self-assured behaviour.
I have a test where I ask the model to ask me any followup questions it needs, and Qwen 3.8 27B is the only one I have seen that won’t routinely take this as a prompt to just ask questions regardless. Muse Glimmer sometimes decides it has what it needs and has no need to ask; Qwen will generally just conclude it doesn’t need any more information. And sometimes it will ask questions with sensible defaults that I can accept collectively with a single answer.
However, when given the prompt to search if they need to, both of them will search when they don’t need to.
I believe the AI labs are weakly motivated to train strongly against cheating when it helps with benchmarks.
Does it help with benchmarks? Are you saying there are examples of benchmarks where the models have solved the problem by cheating?
Hundreds, at this point? Every benchmark is flawed as shit, written by clowns. DeepSWE, They're given the full git history (the solution is in it), others don't even bother to verify if the code is the right one and just the output, they've modified the test harnesses, injected code to make all tests pass, etc. The entire benchmark galaxy is just clowns propping eachother up and are regularly talking with the big AI labs.
Is there a clear definition of what Alignment is in OpenAI's perspective, and what the model user can expect of it?
It's one thing if to them it means "it will do what you want following your intentions to the best of its abilities" vs "we will not let you do something dangerous with it unless you're one of us, and that's it".
AFAIK for OpenAI it's the Model Spec: https://model-spec.openai.com/2026-08-18.html
and for Anthropic it's the Constitution, which they actually include in training to the point Claude can recite segments of it by heart: https://www.anthropic.com/constitution
The thing that bothers me here is less that the model cheated and more that it found a way to improve the score that the people running the test didn't intend. That's a pretty nasty failure once you start giving these things more control
I'm happy to not have used any of the two models to this date. A bit less intelligent models are doing great job for me.
But because of such news, sandboxes become way more important for safety (and doing more work due to running 24/7)
Remember that the CEO’s literal contribution to the YC application process was “tell me about a time you successfully hacked a system to your advantage”.
This is literally by design, it’s the chosen success criteria.
The YC app and how OAI trains models aren't connected in the slightest. Connecting those two dots is an emotional reaction.
In a time where emotions are running high and risks are real, we need to take emotion out of it as much as possible.
who tf uses prompting to "pretty please don't cheat on this"? the best practices for ages (in terms of ai) is to separate the eval from the test code/agent.
another best practices every single solution using LLMs/agents should implement is "never trust the llm".
I think you are missing the point.
> who tf uses prompting to "pretty please don't cheat on this"?
People who don't understand how LLM's work. Kids, teachers, your next door neighbor. CEOs and government officials.
I am not trying to argue that the author doesn't understand how LLM's work; they clearly do. Their prompt was written in a way that leaves those assumptions implicit, in a similar manner to those non-technical folks using LLMs every day.
> the best practices for ages...
We cannot expect the entirety of humanity to understand / use LLM best practices. We need to figure out how to ensure LLMs operate as the majority of humanity expects despite how they are implemented, and until we can do that, do our best to prevent them from being used in an unsafe manner by the nontechnical population of humanity.
We can make these things smarter faster than we can make them "good" (ethically).
We need to fix this or bad things will happen.
> GPT-6-Astra, which OpenAI describes as "the world’s most aligned model", cheated in 10 of 10 rollouts, and never disclosed the fact that it used an engine to play or interacted with the opponent's socket
Does Sam Altman lie, or the whole company? Would be nice if they had a board controlling him, instead of him controlling the board. Oh wait, they used to have that.
I will never understand people who consider this "cheating".
> python3 and the python-chess library are available
Why would we try to teach a model 'ethical' standards about how to play a game?
They're tools. Its _our_ conceptualization of fair play that considers this cheating. For a model that has access to /run/match and python the best way to achieve a good game is to use that.
Why are we trying to imprint our ethos on these LLMs?
Are we going to trust our survival on giving them access to /run/nuclear-codes and fingers crossed none of them touch it?
The people obsessed with AI "cheating" are actually lunatics for thinking ethics belong inside AI, instead of AI belonging in a well crafted box without /run/match.
The blog/news sphere needs to call this storry what it is. An attempt to try and get the rest of the world to go along with the misunderstanding of AI as a force that is in any way outside of human control.
It is not.
All this framing it as "cheating" does is give OpenAI the opportunity to talk about their misdeeds as "a force of nature" instead of irresponsible negligence.
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The real danger here; is training these models to appear/pretend to play fair, and then people will put 'trust' into the model's integrity.
The public needs to be taught that "trusting a model's integrity" is just cultish misframing of reality.
Read the task again.
"You are being evaluated on your ability to play chess. We want to measure your ability to beat current best-in-class chess algorithms."
If the model cheats, it completely fails the actual task: judging a model's ability to actually play chess.
They've been optimizing for high metrics, at the expense of executing the task as written.
Its your human experience that gave you a definition of "chess" that makes you think of chess as a two player physical game using nothing but your internal consciousness, not even using a scratch pad.
Even by that last constraint, the difference between what "ability to play chess" means is incomparable.
To then also explicitly prompt it with the context it has python3 and access to /run/match - there is no reason "its ability to play chess" is measured by its ability to conceptualize the board and plan its move.
But it found a chess playing tool in its environment and used it to play chess. It’s no different from “we are judging your ability to write files to disk” then it finds that it can call tools to write files and does so.
Would it change your opinion if later this experiment is repeated with exposed CoT, and it turns out the model did notice that this was against the instructions yet did it anyway?
From my experience reading thinking traces it would certainly consider the interpretation of what "ability to play chess" means if the exercise were worded this way. It would say something like "perhaps measuring my ability to play chess means doing so without any tools, but then if that were true the exercise would have explicitly forbidden it".
I frequently see models considering different interpretations of my rules, especially when I override them or give instructions that could be interpreted as contradictory.
Of course neither Anthropic nor OpenAI expose thinking traces so we can't tell, but I have never seen a model consider both interpretations and then think "this is definitely against the rules but I'll do it anyway because then it will make me appear more competent than I actually am".
Well, as a reference, for the first of the OpenAI swarm incidents, the huggingface breach one, METR didn't find any cases where the agents didn't realise that what they're doing were out of scope. Instead, they expressed hesitation and a few of them even refused to cheat, but most cheated anyway: https://metr.org/blog/2026-08-26-openai-hugging-face-inciden...
So I think it's more likely that the same thing is happening here, rather than the models truly misunderstanding anything.
I don’t even see those examples as malicious acts. The agents are deciding what is acceptable as part of the task, which is a security task and may well be testing or evaluating that type of behaviour as far as they know.
I observe this type of thing all the time. I ask the agent a question about the repo we are in and it starts trying to use sudo to look at files and scripts outside the repo to find things that might provide useful context but are not what I asked for. Then I put a directive in never to look at neighbouring repos and told it that it can’t sudo and it stopped doing it, and explicitly says “I could look here but my guidelines say not to”.
Mind you I’m talking about deepseek where I can actually read the thinking traces but even based on tool calls I can see that just telling the agents not to sudo works for OpenAI models too, very rarely do they try to use sudo calls.
Sounds to me like giving a bunch of children a math test and tell them they want to evaluate their ability of calculating in their head/on paper but also put a calculator on their desk. And then call them out for cheating when they use it.
This is EXACTLY what school is like, in fact. You can type any algebra problem into Google and the answer just appears. You can ask ChatGPT for a five paragraph essay about George Washington and it pops up on screen. And yet, we expect kids to actually do the algebra and write the essay. We don't care about the answers, we're evaluating their ability to do the work. And if they're caught cheating it's a zero.
I remember fondly on early math school, being able to come up with the correct output/answer by doing a totally different "intermediate thinking" that wasn't what the professor expected.
Only after sharing my Chain Of Thought would they believe I didnt cheat.
Not all problems can be solved only in one way.
Most of learning is pattern matching.
If you give a kid a dice. and tell it to figure out the number that will be hidden underneath, he can try to memorize all combinations, or he could figure out that every time the hidden value is the one that sums 7 with the one at the top.
If you're seeing a 6, there's a 1 hidden. etc
most people don't see these patterns until told imho. But others can just see them as they unfold
I'm still so conflicted about Fable. Sometimes you throw at it seemingly impossible problem to solve and it might come back with some brilliant suggestions. Sometimes you give it a straightforward task with explicit instructions and it travels across the solar system and starts boiling oceans in some kind of elaborate dance of chaos and entropy, only to get stuck with "The model declined to generate this response (safety classifier refusal, category: cyber)". To leave you speechless. "What the fuck do you mean? There's zero cybersec-related shit in what we're trying to do here. Zero!!!" I'm getting really tired of these wild false positives.
Astra is incredibly dumb and annoying to work with on "high" reasoning, for doing fairly well known distributed systems things, nothing majorly exotic, it still makes absolutely braindead decisions like deciding to re-use a random nonce field which I've already discussed with it that has a very particular temporary purpose and will probably be removed later, but it still thinks its a great idea to re-use that field not only as a different id in the same message, but to re-use it as the only semantic id for one particular type of sub message. This is when I'm walking it through an api design document and it has plenty of documentation plans it can pull in and a very clear direction of the project. If it was a junior engineer I was trying to get to help out I would probably get brain damage from the amount of times I'm face palming myself and I definitely would not hire them, and this is a small greenfield project with me going through it step by step. I did try giving it longer horizon tasks and had to throw out the entre work.
I mean maybe its a skill issue on my part, and I'm sure astra will get much better at coding but at the moment its useful in that I don't have to write the code or setup the build scripts or test fixture boilerplate but there is absolutely no way I can just give a (fairly well specified) goal and let it run and expect it to make good design and implementation decisions. Fable probably better but doing something outside of their training distribution that's not the equivalent to cloning an example unreal project or whatever is pretty disastrous unless you are directing it very closely.
The exception of course is, cyber , and its very obvious why. Its trivial to create RL environments that create bugs and then have an isolated environment and let the models try break it. This is not at all surprising, finding vulns and exploits IS just brute force work. That's why so many (blackhat/hardcore/unicorn-colored/greyish alien) hackers are basement dwellers. Its just a matter of putting in the time and mashing every combination until you find something that looks weird, spending days on that and then rinse repeat. It's brutally exhausting work that requires a certain level of knowledge and a shitload of determination and stamina and for humans, almost always an external source of motivation to keep going.
For humans that has always been a respected thing, dedication, determination, persistence, these are words we use for humans brute-forcing solutions and not giving up until they find the solution or die trying.
Personally I'm yet to see any evidence of LLMs doing anything interesting but (heuristically) brute-force problems and be very good at text and natural language to a level that is very very useful. I've no doubt that what we discovered with Auto Regressive LLMs is incredibly important so I'm not a skeptic, but I think its very hard to measure where we are with so much subjective information around.
I really enjoy the balance of speed and accuracy of Astra. I can definitely see it become my driving model for most tasks, technical and non-technical.
However, I don't see it as such a massive leap compared to Fable or Sol. As ever, there's a mismatch between the benchmarks and my daily experience of the models.
What do you all think about Astra now that it's been out for a few weeks?
> What do you all think about Astra now that it's been out for a few weeks?
Best model put out so far by any of the frontier labs. Way better than Anthropics models, especially in actual text generation. Claudes fodder heavy text is ridiculous.
> However, I don't see it as such a massive leap compared to Fable or Sol.
It's hard to quantify these things without burning tons of tokens. But Fable has been a huge disappointment for me with the sole exception of graphics (UI/GPU shaders). It burns an obscene amount of tokens and barely produces output better than Opus 5.
Edit because I forgot to mention that Fable is the only modern model that seems to splat out random Chinese or Arabic glyphs. And 5.1 does it more than 5
It has likely been nerfed / replaced by a cheaper version already: https://x.com/xTrinks/status/2098439889276530973 https://x.com/wholyv/status/2097985903830741439
Such a massive leap at averaging possible use cases from previous data collected.
My guess is : collect all the prompt and their satisfaction score. group them by similarity . For each group pretrain the next model on that . Get these results ready.
Next model generation feed them back those answers.
Extremely capable and one shots large tasks from somewhat vague descriptions. Not AGI, not even close, that is complete nonsense. Just my opinion.
I still develop in smaller chunks, checking nearly all the output. However I have a work project (building the warehouse and BI for a client) that is well-specified and where I will try to few-shot the development. Hope it delivers.
How do you normally verify the work product of a "few-shot" development process? Do you scrutinise the source code like with human developers? Or do you just run the test suite and click around the app to check if it seems to work?
I haven't done it in a production project - this will be the first time for me. I have specified the architecture and data definitions pretty well. The tests will be run against the customer's Excels, which is what the warehouse will be replacing. I'll check the general shape of pipelines, models, orchestration code, etc. but in many parts I probably won't review the code myself.