Group-relative reinforcement learning with verifiable rewards (RLVR) often wastes the most informative data it already has the failures. When all rollouts are wrong, gradients stall; when one happens to be correct, the update usually ignores why the others are close-but-wrong, and credit can be misassigned to spurious chains. We present CARE (Contrastive Anchored REflection), a failure-centric post-training framework for multimodal reasoning that turns errors into supervision. CARE combines: (i) an anchored-contrastive objective that forms a compact subgroup around the best rollout and a set of semantically proximate hard negatives, performs within-subgroup z-score normalization with negative-only scaling, and includes an all-negative rescue to prevent zero-signal batches; and (ii) Reflection-Guided Resampling (RGR), a one-shot structured self-repair that rewrites a representative failure and re-scores it with the same verifier, converting near-misses into usable positives without any test-time reflection. CARE improves accuracy and training smoothness while explicitly increasing the share of learning signal that comes from failures. On Qwen2.5-VL-7B, CARE lifts macro-averaged accuracy by 4.6 points over GRPO across six verifiable visual-reasoning benchmarks; with Qwen3-VL-8B it reaches competitive or state-of-the-art results on MathVista and MMMU-Pro under an identical evaluation protocol.
Anchored Policy Optimization: Mitigating Exploration Collapse Via Support-Constrained Rectification
Beijing University of Posts and Telecommunications（北京邮电大学）；Southern University of Science and Technology（南方科技大学）；Alibaba Group（阿里巴巴集团）；Shanghai University of Finance and Economics（上海财经大学）
CARE: Towards Clinical Accountability in Multi-Modal Medical Reasoning with an Evidence-Grounded Agentic Framework
Microsoft Research Asia（微软亚洲研究院）；Department of Biomedical Engineering, Yale University（耶鲁大学生物医学工程系）；Department of Radiology & Biomedical Imaging, Yale University（耶鲁大学放射学与生物医学成像系）
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）
Rollout-Level Advantage-Prioritized Experience Replay for GRPO
Department of Electrical and Computer Engineering, Seoul National University（首尔国立大学电子与计算机工程系）；Interdisciplinary Program in AI, Seoul National University（首尔国立大学人工智能跨学科项目）；AIIS, ASRI, INMC, and ISRC, Seoul National University（首尔国立大学人工智能研究所、人工智能研究机构、智能网络与计算中心及人工智能科学研究中心）
Revisiting Entropy Regularization: Adaptive Coefficient Unlocks Its Potential for LLM Reinforcement Learning
State Key Lab of Processors, Institute of Computing Technology, CAS（处理器国家重点实验室，计算技术研究所，中国科学院）；University of Science and Technology of China（中国科学技术大学）；University of Chinese Academy of Sciences（中国科学院大学）；StepFun Inc（StepFun公司）
Rethinking Entropy Regularization in Large Reasoning Models
Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Fudan University（复旦大学）；Shanghai Jiao Tong University（上海交通大学）；Chinese University of Hong Kong（香港中文大学）
