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Matching papers

On-Policy Self-Distillation without Any Supervision

183 upvotes · 9 AUG 2026 · Yijiang Li, Bingyang Wang, Yijun Liang et al.

This paper shows how to make large language models improve themselves without needing external guidance or supervision, by using their own internal consistency to correct mistakes. Practitioners might care about this because it could lead to more robust and self-sufficient AI models.

self-distillationon-policy methodsunsupervised learninginternal consistencyreinforcement learningcontinual learning

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