Reinforcement learning with verifiable rewards (RLVR) is a simple but powerful paradigm for training LLMs: sample a completion, verify it, and update. In practice, however, the verifier is almost never clean--unit tests probe only limited corner cases; human and synthetic labels are imperfect; and LLM judges (e.g., RLAIF) are noisy and can be exploited--and this problem worsens on harder domains (especially coding) where tests are sparse and increasingly model-generated. We ask a pragmatic question: does the verification noise merely slow down the learning (rate), or can it flip the outcome (fate)?
To address this, we develop an analytically tractable multi-armed bandit view of RLVR dynamics, instantiated with GRPO and validated in controlled experiments. Modeling false positives and false negatives and grouping completions into recurring reasoning modes yields a replicator-style (natural-selection) flow on the probability simplex. The dynamics decouples into within-correct-mode competition and a one-dimensional evolution for the mass on incorrect modes, whose drift is determined solely by Youden's index J=TPR-FPR. This yields a sharp phase transition: when J>0, the incorrect mass is driven toward extinction (learning); when J=0, the process is neutral; and when J<0, incorrect modes amplify until they dominate (anti-learning and collapse). In the learning regime J>0, noise primarily rescales convergence time ("rate, not fate"). Experiments on verifiable programming tasks under synthetic noise reproduce the predicted J=0 boundary. Beyond noise, the framework offers a general lens for analyzing RLVR stability, convergence, and algorithmic interventions.
RLVR Training of LLMs Does Not Improve Thinking Ability for General QA: Evaluation Method and a Simple Solution
National Key Laboratory for Novel Software Technology（新型软件技术国家重点实验室）；School of Artificial Intelligence, Nanjing University（南京大学人工智能学院）；Huawei Technologies Co., Ltd.（华为技术有限公司）
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）
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）
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）
Auditing Data Membership in Reinforcement Learning With Verifiable Rewards
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Shanghai Jiao Tong University（上海交通大学）；Shandong University（山东大学）；Xidian University（西安电子科技大学）；Wuhan University（武汉大学）
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）
Combinatorial Synthesis: Scaling Code RLVR via Atomic Decomposition and Recombination
Chinese Information Processing Laboratory, Institute of Software, Chinese Academy of Sciences（中国科学院软件研究所信息处理实验室）；University of Chinese Academy of Sciences（中国科学院大学）；Lero the Research Ireland Centre for Software, University of Limerick（利默尼克大学爱尔兰软件研究中心）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；The Hong Kong University of Science and Technology（香港科技大学）
Learning from Failures: Correction-Oriented Policy Optimization with Verifiable Rewards
Chinese Information Processing Laboratory, Institute of Software, Chinese Academy of Sciences（中国科学院软件研究所信息处理实验室）；University of Chinese Academy of Sciences（中国科学院大学）；Xiaohongshu Inc（小红书公司）
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络空间安全学院）
CLIPO: Contrastive Learning in Policy Optimization Generalizes RLVR
Qwen Large Model Application Team, Alibaba Institute of Automation, Chinese Academy of Sciences（文勤大模型应用团队，阿里巴巴自动化研究所，中国科学院）；Qwen Large Model Application Team, Alibaba（文勤大模型应用团队，阿里巴巴）
MoDoMoDo: Multi-Domain Data Mixtures for Multimodal LLM Reinforcement Learning
Brown University（布朗大学）；Salesforce AI Research（Salesforce AI研究院）；NVIDIA Research（英伟达研究院）；Carnegie Mellon University（卡内基梅隆大学）；Princeton University（普林斯顿大学）；Texas A&M University（德克萨斯A&M大学）；California Institute of Technology（加州理工学院）；MIT CSAIL（麻省理工学院计算机科学与人工智能实验室）
Generalization of RLVR Using Causal Reasoning as a Testbed
Johns Hopkins University（约翰霍普金斯大学）；University of Maryland, College Park（马里兰大学学院公园分校）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Microsoft Research Asia（微软亚洲研究院）；Toyota Technological Institute at Chicago（芝加哥丰田技术研究所）
