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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

Co-RL: Unsupervised Reasoning Emerges from Diverse Cohort in Multi-agent RL

92 upvotes · 19 AUG 2026 · Yunhao Yang, Yuexin Bian, Yunjie Tian et al.

This paper introduces Co-RL, a framework for unsupervised multi-agent reinforcement learning that enables diverse and accurate reasoning in language and vision-language models. Practitioners can use Co-RL to improve their models' ability to reason and respond without relying on expensive ground-truth supervision.

multi-agent RLunsupervised learningreasoningreinforcement learninglanguage modelsvision-language models