[rahimnathwani]

From the Deepseek R1 paper:

  For distilled models, we apply only SFT and do not include an RL stage, even though incorporating RL could substantially boost model performance. Our primary goal here is to demonstrate the effectiveness of the distillation technique, leaving the exploration of the RL stage to the broader research community.

The impression I got from the paper, although I don't think it was explicitly stated, is that they think distillation will work better than training the smaller models using RL (as OP did).

[nielsole]

> We demonstrate that the reasoning patterns of larger models can be distilled into smaller models, resulting in better performance compared to the reasoning patterns discovered through RL on small models

I found this statement from the paper to be at odds with what you cited, but I guess they mean SFT+RL would be better than either just SFT and RL

[rahimnathwani]

I think they're saying that some reasoning patterns which large models can learn using only RL (i.e. without the patterns existing in the training data), can't be learned by smaller models in the same way. They have to be 'taught' through examples provided during SFT.