Last update over a year ago, so I hope (2025) gets added to the title:
> [2025/05/26] (Step 1 completed!) We release Mixture-of-Thoughts--a curated reasoning dataset of 350k verified traces distilled from R1. The dataset spans tasks in mathematics, coding, and science, and is designed to teach language models to reason step-by-step. We also provide a recipe to train OpenR1-Distill-7B, which replicates the reasoning capabilities of deepseek-ai/DeepSeek-R1-Distill-Qwen-7B and marks the completion of step 1 in the Open R1 project.
Doesn't look like they managed to actually reproduce R1, and only stopped on Step 1 out of their 3-step plan.
One of my favorite code comments of all time is still in the src:
"# TODO: implement a proper validator to compare against ground truth. For now we just check for exact string match on each line of stdout." [1]
This was one of my chief complaints about the entire R1 news cycle, it felt like no one actually read the technical report. They were being heralded for their openness, but they left out the most meaningful details that you'd need to reproduce their work.
I'm not really familiar with either, but I'm more familiar with Olmo. My impression is Nemotron is newer -- why is it less applicable? Is it not totally open like Olmo?
Open-source data coverage: The released datasets cover an estimated 8–10T tokens
(~40–50% of the internal 25T blend). Missing categories include code (~14% of blend),
nemotron-cc-code (~2%), crawl++ (~2%), and academic text (~2%). Users should
supplement with their own data for these categories and adjust train_iters
accordingly.
K2 Think V2 is another fully open model like Olmo, with full datasets released.
Note that the Nemotron models are generally stronger than Olmo and K2 Think V2 (according to Artificial Analysis benchmarks), and there is a lot of overlap in their datasets (lots of datasets are based on the same sources with different filtering, Olmo and K2 Think V2 both have used some Nemotron datasets).
But yeah, Nemotron is a modern and fairly capable LLM, even the 122b is more capable than Deepseek R1 (a 671b model) on most benchmarks, and there's also the recently released 550b Ultra now.
It does have a fully open training recipe, just some data missing from its datasets, but if you want a fully open pipeline it's going to be a good place to start, you just need to find some more data to fill in the datasets to get up to the token count with reasonably high quality data.
Check out OpenThoughts. It has a widely used dataset, a model that beats the deepseek's smaller reasoning models, and a paper that talks in detail about the data curation methodology.
Last update over a year ago, so I hope (2025) gets added to the title:
> [2025/05/26] (Step 1 completed!) We release Mixture-of-Thoughts--a curated reasoning dataset of 350k verified traces distilled from R1. The dataset spans tasks in mathematics, coding, and science, and is designed to teach language models to reason step-by-step. We also provide a recipe to train OpenR1-Distill-7B, which replicates the reasoning capabilities of deepseek-ai/DeepSeek-R1-Distill-Qwen-7B and marks the completion of step 1 in the Open R1 project.
Doesn't look like they managed to actually reproduce R1, and only stopped on Step 1 out of their 3-step plan.
One of my favorite code comments of all time is still in the src:
"# TODO: implement a proper validator to compare against ground truth. For now we just check for exact string match on each line of stdout." [1]
This was one of my chief complaints about the entire R1 news cycle, it felt like no one actually read the technical report. They were being heralded for their openness, but they left out the most meaningful details that you'd need to reproduce their work.
[1] https://github.com/huggingface/open-r1/blob/1416fa0cf21595d2...
Reminds me of my days in a computational physics PhD program.
> This was one of my chief complaints about the entire R1 news cycle
For me it was the headline that a group of students replicated GPT-3 for $5000
If you really want to see fully open training pipelines for modern LLMs, Olmo and to a lesser extent Nemotron are what you should look at.
https://github.com/allenai/OLMo
https://github.com/NVIDIA-NeMo/Nemotron
I'm not really familiar with either, but I'm more familiar with Olmo. My impression is Nemotron is newer -- why is it less applicable? Is it not totally open like Olmo?
Olmo releases their full datasets.
Nemotron only releases portions of some of their datasets, like the source code dataset that they pretrain on.
For example, from https://docs.nvidia.com/nemotron/latest/nemotron/super3/pret... :
K2 Think V2 is another fully open model like Olmo, with full datasets released.Note that the Nemotron models are generally stronger than Olmo and K2 Think V2 (according to Artificial Analysis benchmarks), and there is a lot of overlap in their datasets (lots of datasets are based on the same sources with different filtering, Olmo and K2 Think V2 both have used some Nemotron datasets).
But yeah, Nemotron is a modern and fairly capable LLM, even the 122b is more capable than Deepseek R1 (a 671b model) on most benchmarks, and there's also the recently released 550b Ultra now.
It does have a fully open training recipe, just some data missing from its datasets, but if you want a fully open pipeline it's going to be a good place to start, you just need to find some more data to fill in the datasets to get up to the token count with reasonably high quality data.
Check out OpenThoughts. It has a widely used dataset, a model that beats the deepseek's smaller reasoning models, and a paper that talks in detail about the data curation methodology.
https://www.open-thoughts.ai/
neat
Too old now
"This will likely involve curating new, large-scale datasets for math, reasoning, and code.". ... everybody likes to hand-wave on this .
What is the estimated cost these days to train something like this to conclusion?