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Distilled Reinforcement Learning for LLM Post-training

8 upvotes · 19 JUL 2026 · Chen Wang, Zhaochun Li, Jionghao Bai et al.

This paper proposes a new method called Distilled Reinforcement Learning that improves large language model post-training by providing fine-grained guidance to transfer new knowledge from a teacher model to a student model. Practitioners might care because it outperforms standard reinforcement learning and on-policy distillation methods in terms of knowledge transfer and model performance.

reinforcement learningcontinual learninglarge language modelspost-trainingteacher-student models