We are reproducing the full DeepSeek R1 data and training pipeline so everybody can use their recipe. Instead of doing it in secret we can do it together in the open!
š§Ŗ Step 1: replicate the R1-Distill models by distilling a high-quality reasoning corpus from DeepSeek-R1.
š§ Step 2: replicate the pure RL pipeline that DeepSeek used to create R1-Zero. This will involve curating new, large-scale datasets for math, reasoning, and code.
š„ Step 3: show we can go from base model -> SFT -> RL via multi-stage training.
I was initially pretty sceptical about Meta's Coconut paper [1] because the largest perf gains were reported on toy linguistic problems. However, these results on machine translation are pretty impressive!