Learning to Explore with Parameter-Space Noise: A Deep Dive into Parameter-Space Noise for Reinforcement Learning with Verifiable Rewards
Shanghai Innovation Institute, Shanghai, China（上海创新研究院）；College of Future Information Technology, Fudan University, Shanghai, China（未来信息科技学院，复旦大学）；Shanghai AI Laboratory, Shanghai, China（上海人工智能实验室）；The Chinese University of Hong Kong, Hong Kong, China（香港中文大学）
Reinforcement Learning with Verifiable Rewards (RLVR) improves LLM reasoning, yet growing evidence indicates an exploration ceiling: it often reweights existing solution traces rather than discovering new strategies, limiting gains under large sampling budgets (e.g., pass-at-256). We address this limitation with PSN-RLVR, which perturbs policy parameters before rollout generation to induce temporally consistent, trajectory-level exploration that better preserves long-horizon chain-of-thought coherence than action-space noise. To mitigate the resulting sampling-update mismatch, we incorporate truncated importance sampling (TIS). To avoid expensive KL-based adaptive noise control, we propose a computationally efficient real-time adaptive noise scheduler driven by a lightweight surrogate that combines semantic diversity with normalized self-certainty. Instantiated on GRPO, a widely used RLVR method, PSN-GRPO consistently expands the effective reasoning capability boundary across multiple mathematical reasoning benchmarks and model families, yielding higher pass-at-k under large sampling budgets and outperforming prior exploration-oriented RLVR methods (e.g., Pass-at-k-style training) while remaining orthogonal and thus composable for additional gains.
GUI-GENESIS: Automated Synthesis of Efficient Environments with Verifiable Rewards for GUI Agent Post-Training
Key Lab of HCST (PKU), MOE；SCS, Peking University, Beijing, China；Tencent Inc., Shenzheng, China；Hong Kong University of Science；Department of Computer Science, University of Texas at Dallas, Richardson, USA；School of Computing Science, Simon Fraser University, Burnaby, BC, Canada
Contextual Rollout Bandits for Reinforcement Learning with Verifiable Rewards
School of Computer Science and Engineering, Beihang University（北京航空航天大学计算机科学与工程学院）；School of Artificial Intelligence, Beihang University（北京航空航天大学人工智能学院）；Huawei（华为）
BeamPERL: Parameter-Efficient RL with Verifiable Rewards Specializes Compact LLMs for Structured Beam Mechanics Reasoning
Department of Mechanical Engineering（机械工程系）；Massachusetts Institute of Technology（麻省理工学院）；Department of Civil and Environmental Engineering（土木与环境工程系）；Schwarzman College of Computing（施瓦茨曼计算学院）
Position: The Hidden Costs and Measurement Gaps of Reinforcement Learning with Verifiable Rewards
Stanford University（斯坦福大学）；UC Berkeley（加州大学伯克利分校）；The University of Tokyo（东京大学）；RIKEN AIP（理化学研究所AIP）；Waseda University（早稻田大学）；Georgia Tech（佐治亚理工学院）；Northwestern University（西北大学）；UCLA（加州大学洛杉矶分校）；UNC Chapel Hill（北卡罗来纳大学教堂山分校）；Yale University（耶鲁大学）；University of Waterloo（滑铁卢大学）；Independent Researcher（独立研究者）；CUHK（香港中文大学）；UT Southwestern Medical Center（西南医学中心）；National University of Singapore（新加坡国立大学）；UIUC（伊利诺伊大学厄巴纳-香槟分校）；Amazon AWS AI（亚马逊AWS人工智能）
STRIDE: Strategic Trajectory Reasoning via Discriminative Estimation for Verifiable Reinforcement Learning
Kean University（基恩大学）；Case Western Reserve University（凯斯西储大学）；University of Texas at Austin（德克萨斯大学奥斯汀分校）；The Ohio State University（俄亥俄州立大学）；Tongji University（同济大学）；Duke Kunshan University（昆山杜克大学）；Royal Melbourne Institute of Technology（皇家墨尔本理工大学）
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）
Promoting Efficient Reasoning with Verifiable Stepwise Reward
Meituan（美团）；Fudan University（复旦大学）；Beijing Institute of Technology（北京理工大学）；University of Science and Technology of China（中国科学技术大学）；East China Normal University（华东师范大学）
