HF Journal Club: On-Policy Distillation Transfers RL Gains to Large Models at Low Compute
Hugging Face · youtube · 2026-08-12
The Hugging Face research team discussed the paper Weak-to-Strong Generalization via Direct On-Policy Distillation in their Journal Club.
The study proposes a highly cost-effective method to transfer the benefits of reinforcement learning (RL) from small models to much larger ones. Instead of directly imitating the smaller model, it measures how RL changed the small model's policy and uses that change as a dense reward signal to train the larger model.
The results show that this form of weak-to-strong generalization can match or outperform direct RL on the larger model at a fraction of the compute cost.
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