Open-R1: Update #1

It’s been two weeks since the release of DeepSeek R1 and just a week since we started the open-r1 project to replicate the missing pieces, namely the training pipeline and the synthetic data. This post summarizes:

• the progress of Open-R1 to replicate the DeepSeek-R1 pipeline and dataset
• what we learned about DeepSeek-R1 and discussions around it
• cool projects the community has built since the release of DeepSeek-R1

It should serve both as an update on the project and as a collection of interesting resources around DeepSeek-R1.

Progress after 1 Week

Let’s start by looking at the progress we made on Open-R1. We started Open-R1 just one week ago and people across the teams as well as the community came together to work on it and we have some progress to report.

Evaluation

The first step in reproduction is to verify that we can match the evaluation scores. We are able to reproduce Deepseek's reported results on the MATH-500 Benchmark:

Model	MATH-500 (HF lighteval)	MATH-500 (DeepSeek Reported)
DeepSeek-R1-Distill-Qwen-1.5B	81.6	83.9
DeepSeek-R1-Distill-Qwen-7B	91.8	92.8
DeepSeek-R1-Distill-Qwen-14B	94.2	93.9
DeepSeek-R1-Distill-Qwen-32B	95.0	94.3
DeepSeek-R1-Distill-Llama-8B	85.8	89.1
DeepSeek-R1-Distill-Llama-70B	93.4	94.5

You can find the instructions to run these evaluations in the open-r1 repository.

One observation we have made is the enormous size of the generations from the DeepSeek models, which makes even evaluating the model challenging. Here we show DeepSeek-R1 response lengths in the OpenThoughts dataset:

Distribution of R1’s responses shows that they are on average very long with the average response being 6,000 tokens long and some responses containing more than 20,000 tokens. Worth noting that the average page contains ~500 words and one token is on average slightly less than a word, which means the many reponses are over 10 pages long. (src: https://x.com/gui_penedo/status/1884953463051649052)