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Matching papers

Does On-Policy Distillation Really Distill? From Noisy Teacher to Self-Improvement

89 upvotes · 31 AUG 2026 · Yi Ding, Ruqi Zhang

This paper investigates whether on-policy distillation (OPD) truly improves student policies by analyzing the effects of noisy teacher supervision. It finds that OPD works by suppressing low-probability tokens, which can be achieved without a teacher, and introduces a new method called On-Policy Self-Adaptation (OPSA) that outperforms OPD and traditional reinforcement learning methods.

reinforcement learningon-policy methodsself-supervised learningpolicy distillationcontinual learning

DAPD: Dual-Anchored Policy Distillation

51 upvotes · 3 AUG 2026 · Jianyu Wu, Yizhou Wang, Encheng Su et al.

This paper addresses a problem in self-distillation, where a student model learns to mimic the behavior of a privileged teacher, but performs poorly at inference due to a "privilege illusion". The authors propose a new method, Dual-Anchored Policy Distillation, to resolve this issue and improve performance.

self-distillationon-policy learningpolicy distillationprivilege illusioninformation asymmetryreinforcement learning