Test-time reinforcement learning generates multiple candidate answers via repeated rollouts and performs online updates using pseudo-labels constructed by majority voting. To reduce overhead and improve exploration, prior work introduces tree structured rollouts, which share reasoning prefixes and branch at key nodes to improve sampling efficiency. However, this paradigm still faces two challenges: (1) high entropy branching can trigger rollout collapse, where the branching budget concentrates on a few trajectories with consecutive high-entropy segments, rapidly reducing the number of effective branches; (2) early pseudo-labels are noisy and biased, which can induce self-reinforcing overfitting, causing the policy to sharpen prematurely and suppress exploration. To address these issues, we propose Entropy Confidence Hybrid Group Relative Policy Optimization (ECHO). During rollout, ECHO jointly leverages local entropy and group level confidence to adaptively control branch width, and further introduces online confidence-based pruning to terminate persistently low confidence branches, avoiding high entropy traps and mitigating collapse. During policy updates, ECHO employs confidence adaptive clipping and an entropy confidence hybrid advantage shaping approach to enhance training robustness and mitigate early stage bias. Experiments demonstrate that ECHO achieves consistent gains on multiple mathematical and visual reasoning benchmarks, and generalizes more effectively under a limited rollout budget.
EchoRL: Reinforcement Learning via Rollout Echoing
Munich Center for Machine Learning（慕尼黑机器学习中心）；Huawei Heisenberg Research Center（华为海森堡研究所以）；University of Arizona（亚利桑那大学）；College of Computing（计算学院）；Data Science, Nanyang Technological University, Singapore（数据科学，南洋理工大学，新加坡）；MemAgents Lab（MemAgents实验室）
Rule-based and reinforcement-learning policies for resource allocation in a pediatric echo lab: a simulation study
Department of Applied and Computational Mathematics and Statistics, University of Notre Dame（应用与计算数学与统计学系，圣约翰大学）；Department of Pediatrics (Cardiology), Stanford University（儿科（心脏病学）系，斯坦福大学）；Department of Bioengineering, Stanford University（生物工程系，斯坦福大学）
ECHO-2: A Large-Scale Distributed Rollout Framework for Cost-Efficient Reinforcement Learning
The University of Hong Kong（香港大学）；Fudan University（复旦大学）；Gradient；University of Edinburgh（爱丁堡大学）；Soochow University（苏州大学）；Technical University of Darmstadt（达姆施塔特技术大学）；University of the Chinese Academy of Sciences（中国科学院大学）
Listening to the Echo: User-Reaction Aware Policy Optimization via Scalar-Verbal Hybrid Reinforcement Learning
State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, CAS（多模态人工智能系统国家重点实验室，自动化研究所，中国科学院）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）
Reinforcement Learning for Unsupervised Domain Adaptation in Spatio-Temporal Echocardiography Segmentation
Department of Computer Science, University of Sherbrooke（谢布鲁克大学计算机科学系）；INSA, Universite Claude Bernard Lyon 1, CNRS UMR 5220, Inserm U1206, CREATIS（里昂1大学INSA、CNRS UMR 5220、Inserm U1206、CREATIS）；Dep. of Software and Information Technology Engineering, École de technologie supérieure（蒙特利尔工程学院软件与信息技术工程系）；Institut Universitaire de France (IUF)（法国国家科学院（IUF））
On the effectiveness of reward functions in reinforcement learning for confidence calibration of large language models
School of Computing National University of Singapore（新加坡国立大学计算机学院）；Institute of Advanced Intelligence and Computing（高级智能与计算研究所）；Agency for Science, Technology and Research（科技研究局）；Department of Electrical & Computer Engineering（电气与计算机工程系）
Vector Institute for Artificial Intelligence（向量人工智能研究所）；School of Data Science, The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳）数据科学学院）；Huawei Technologies Canada（华为技术加拿大公司）；Cheriton School of Computer Science, University of Waterloo（滑铁卢大学谢里敦计算机科学学院）
C3: Rethinking Process Supervision via Confidence-Guided Credit Correction in Multi-Turn Reinforcement Learning
Beijing University of Posts and Telecommunications（北京邮电大学）；Peking University（北京大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；University of International Business and Economics（对外经济贸易大学）
Decoupling Reasoning and Confidence: Resurrecting Calibration in Reinforcement Learning from Verifiable Rewards
Chinese Information Processing Laboratory, Institute of Software, Chinese Academy of Sciences, Beijing, China（中国科学院软件研究所信息处理实验室）；University of Chinese Academy of Sciences, Beijing, China（中国科学院大学）；Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China（中国科学院大学网络安全学院）；National Computer Network Emergency Response Technical Team/Coordination Center of China, Beijing, China（中国国家计算机网络应急技术配合中心）
Exploration in Knowledge Transfer Utilizing Reinforcement Learning
Institute of Information Theory and Automation, Czech Academy of Sciences（捷克科学院信息理论与自动化研究所）；Faculty of Applied Sciences, University of West Bohemia（西波希米亚大学应用科学学院）
Of Errors and Echoes: Responsibility-Aware Credit Assignment for Tool-Use Agents
University of California, Irvine（加利福尼亚大学欧文分校）；Georgia Institute of Technology（佐治亚理工学院）；Dartmouth College（达特茅斯学院）；Northeastern University（东北大学）；University of Notre Dame（圣母大学）
Beyond Correctness: Confidence-Aware Reward Modeling for Enhancing Large Language Model Reasoning
Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Shanghai Key Laboratory of Data Science, School of Computer Science, Fudan University（上海数据科学关键实验室，计算机科学学院，复旦大学）
Ranking-Aware Calibration for Reliable Multimodal Reinforcement Learning
Dept. of Comp. Sci. & Tech., Institute for AI, BNRist Center, Tsinghua-Bosch Joint ML Center, THBI Lab, Tsinghua University, Beijing（计算机科学与技术系，人工智能研究院，BNRist中心，清华大学-博世联合机器学习中心，THBI实验室，清华大学，北京）；Dept. of Automation, Tsinghua University, Beijing（自动化系，清华大学，北京）
Echo: Towards Advanced Audio Comprehension via Audio-Interleaved Reasoning
IIE, Chinese Academy of Sciences（中国科学院信息工程研究所）；Zhejiang University（浙江大学）；ByteDance China（字节跳动中国）；VCIP & TMCC & DISSec, College of Computer Science, Nankai University（VCIP与TMCC与DISSec，南开大学计算机学院）；School of Information Science and Technology, ShanghaiTech University（上海科技大学信息科学与技术学院）；Department of Psychological and Cognitive Sciences, Tsinghua University（清华大学心理学与认知科学系）；University of Chinese Academy of Sciences（中国科学院大学）
Confidence-Guided Human-AI Collaboration: Reinforcement Learning with Distributional Proxy Value Propagation for Autonomous Driving
Tianjin Key Laboratory of Intelligent Unmanned Swarm Technology and System, School of Electrical and Information Engineering, Tianjin University（天津智能无人群技术与系统重点实验室，电气与信息工程学院，天津大学）；Key Laboratory of System Control and Information Processing, Ministry of Education of China（系统控制与信息处理重点实验室，中华人民共和国教育部）；School of Mechanical Engineering, Shanghai Jiao Tong University（机械工程学院，上海交通大学）
