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Matching papers

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

Confidence Comes from Experience: Experiential Confidence Estimation from Reasoning to Agents

56 upvotes · 15 SEP 2026 · Caiqi Zhang, Xiaochen Zhu, Chengzu Li et al.

This paper proposes a new method for estimating confidence in language models, called XConf, which uses the model's past experiences to inform its confidence, rather than just relying on the current inference process. Practitioners might care about this because it could lead to more reliable and trustworthy deployment of language models.

experiential confidence estimationreasoningagentslanguage modelscontinual learning