Self-Distilled Policy Gradient

Yifeng Liu, Shiyuan Zhang, Yifan Zhang, Quanquan Gu

Self-Distilled Policy Gradient: 27 upvotes on Hugging Face Daily Papers, #13 of 54 papers on 2026-06-04. Day-by-day upvote history.

On-policy self-distillation, where a language model conditions on privileged context to supervise its own generations, is a promising source of dense supervision for sparse-reward reinforcement learning. Actually, it can be instantiated as an auxiliary full-vocabulary student-to-teacher reverse Kullback-Leibler divergence loss. We therefore propose SDPG, a self-distilled policy-gradient framework that combines group-relative verifier advantages with normalized standard deviation, exact full-vocabulary on-policy self-distillation, as well as reference-policy KL regularization. Empirically, SDPG improves stability and performance over RLVR and self-distillation baselines. The code is available at https://github.com/lauyikfung/SDPG.

Paper page on Hugging Face · arXiv

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