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.

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

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/

Too old now

What is the estimated cost these days to train something like this to conclusion?

"This will likely involve curating new, large-scale datasets for math, reasoning, and code.". ... everybody likes to hand-wave on this .

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