Large language models (LLMs) are increasingly deployed as multi-step decision-making agents, where effective reward design is essential for guiding learning. Although recent work explores various forms of reward shaping and step-level credit assignment, a key signal remains largely overlooked: the intrinsic uncertainty of LLMs. Uncertainty reflects model confidence, reveals where exploration is needed, and offers valuable learning cues even in failed trajectories. We introduce SELAUR: Self Evolving LLM Agent via Uncertainty-aware Rewards, a reinforcement learning framework that incorporates uncertainty directly into the reward design. SELAUR integrates entropy-, least-confidence-, and margin-based metrics into a combined token-level uncertainty estimate, providing dense confidence-aligned supervision, and employs a failure-aware reward reshaping mechanism that injects these uncertainty signals into step- and trajectory-level rewards to improve exploration efficiency and learning stability. Experiments on two benchmarks, ALFWorld and WebShop, show that our method consistently improves success rates over strong baselines. Ablation studies further demonstrate how uncertainty signals enhance exploration and robustness.
University of British Columbia（不列颠哥伦比亚大学）；Vector Institute（向量研究所）；Kolors Team, Kuaishou Technology（快手团队）；Carnegie Mellon University（卡内基梅隆大学）；University of Waterloo（滑铁卢大学）；Etude AI；Tsinghua University（清华大学）；Georgia Institute of Technology（佐治亚理工学院）
SPARK: Synergistic Policy And Reward Co-Evolving Framework
Shanghai Jiao Tong University（上海交通大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Fudan University（复旦大学）；The Chinese University of Hong Kong（香港中文大学）；Shanghai Innovation Institute（上海创新研究院）
CoMAS: Co-Evolving Multi-Agent Systems via Interaction Rewards
The Chinese University of Hong Kong（香港中文大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；University of Georgia（佐治亚大学）；National University of Singapore（新加坡国立大学）；Dalian University of Technology（大连理工大学）；University of Oxford（牛津大学）；University of California San Diego（加利福尼亚大学圣迭戈分校）；Shenzhen Loop Area Institute（深圳河套学院）
My FAULT: Self-Diagnosis as Credit Assignment in Self-Evolving Agentic Reinforcement Learning
Alibaba Group（阿里巴巴集团）；Kyoto University（京都大学）；NII LLMC（国立情报学研究所LLMC）；Peking University（北京大学）；University of California, Los Angeles（加州大学洛杉矶分校）；Arizona State University（亚利桑那州立大学）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Tsinghua University（清华大学）；University of the Chinese Academy of Sciences（中国科学院大学）
R-Zero: Self-Evolving Reasoning LLM from Zero Data
Tencent AI Seattle Lab（腾讯AI西雅图实验室）；Washington University in St. Louis（华盛顿大学圣路易斯分校）；University of Maryland, College Park（马里兰大学科廷分校）；The University of Texas at Dallas（德克萨斯大学达拉斯分校）
VisPlay: Self-Evolving Vision-Language Models from Images
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Washington University in St. Louis（华盛顿大学圣路易斯分校）；University of Maryland（马里兰大学）；National University of Singapore（新加坡国立大学）
Learning How and What to Memorize: Cognition-Inspired Two-Stage Optimization for Evolving Memory
University of Science and Technology of China & State Key Laboratory of Cognitive Intelligence（中国科学技术大学 & 认知智能国家重点实验室）；City University of Hong Kong（香港城市大学）；Dalian University of Technology（大连理工大学）；Xi’an Jiaotong University（西安交通大学）
Institute for Infocomm Research, Agency for Science, Technology and Research (A*STAR)（信息通信研究院，新加坡科技研究局（A*STAR））；Center for Data Sciences, International Institute of Information Technology Bangalore (IIITB)（班加罗尔国际信息技术学院数据科学中心）；School of Computer Science and Engineering, Nanyang Technological University（南洋理工大学计算机科学与工程学院）；A*STAR Centre for Frontier AI Research（新加坡科技研究局前沿人工智能研究中心）
PACR: Progressively Ascending Confidence Reward for LLM Reasoning
Korea Advanced Institute of Science and Technology (KAIST)（韩国科学技术院）；Microsoft Research Asia (MSRA)（微软亚洲研究院）；University of Illinois at Urbana-Champaign (UIUC)（伊利诺伊大学厄巴纳-香槟分校）
Jump-Start Reinforcement Learning with Self-Evolving Priors for Extreme Monopedal Locomotion
State Key Laboratory of Intelligent Green Vehicle and Mobility, School of Vehicle and Mobility, Tsinghua University, Beijing, China（智能绿色车辆与移动 state key laboratory，车辆与移动学院，清华大学，北京，中国）；College of AI, Tsinghua University, Beijing, China（人工智能学院，清华大学，北京，中国）；SunrisingAI Ltd., Beijing, China（SunrisingAI有限公司，北京，中国）
