Reward models are critical for reinforcement learning from human feedback, as they determine the alignment quality and reliability of generative models. For complex tasks such as image editing, reward models are required to capture global semantic consistency and implicit logical constraints beyond local similarity. Existing reward modeling approaches have clear limitations. Discriminative reward models align well with human preferences but struggle with complex semantics due to limited reasoning supervision. Generative reward models offer stronger semantic understanding and reasoning, but they are costly at inference time and difficult to align directly with human preferences. To this end, we propose Joint Reward Modeling (JRM), which jointly optimizes preference learning and language modeling on a shared vision-language backbone. This approach internalizes the semantic and reasoning capabilities of generative models into efficient discriminative representations, enabling fast and accurate evaluation. JRM achieves state-of-the-art results on MMRB2 and EditReward-Bench, and significantly improves stability and performance in downstream online reinforcement learning. These results show that joint training effectively bridges efficiency and semantic understanding in reward modeling.
Internalizing Safety Understanding in Large Reasoning Models via Verification
National University of Singapore（国立新加坡大学）；University of Science and Technology of China（中国科学技术大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）
One Policy, Any Budget: Internalizing Budget-Aware Search via Reinforcement Learning
College of Computer Science and Artificial Intelligence, Fudan University（复旦大学计算机与人工智能学院）；College of Software Engineering, Southeast University（东南大学软件学院）；School of Artificial Intelligence, Shanghai Jiao Tong University（上海交通大学人工智能学院）
EnvACE: Internalizing Environment Dynamics via World Rehearsal for Agentic Reinforcement Learning
Shanghai Jiao Tong University（上海交通大学）；Zhejiang University（浙江大学）；National University of Singapore（新加坡国立大学）；Sun Yat-sen University（中山大学）；Central South University（中南大学）；The Chinese University of Hong Kong（香港中文大学）；Tencent Inc.（腾讯公司）
SCOUT: Self-Checking and Recovery-Aware Tool-Thought Agents for Ultra-Long Egocentric Video Reasoning
Sun Yat-sen University（中山大学）；Shenzhen Loop Area Institute（深圳河套学院）；Guangdong OPPO Mobile Telecommunications Corp., Ltd.（广东欧珀移动通信有限公司）；OPPO AI Center（OPPO人工智能中心）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））
CritiqueDriveVLM: From Verifier-Guided Reinforcement Learning to Latent Thought Distillation for Autonomous Driving
State Key Laboratory of Networking and Switching Technology（网络与交换技术国家重点实验室）；School of Digital Media & Design Arts（数字媒体与设计艺术学院）；Beijing University of Posts and Telecommunications（北京邮电大学）
CSRP: Chain-of-Thought Reasoning for Chinese Text Correction via Reinforcement Learning with Efficiency-Aware Rewards
School of Computer Science and Technology, East China Normal University（东华大学计算机科学与技术学院）；Shanghai Institute of Artificial Intelligence for Education, East China Normal University（东华大学教育人工智能研究所）
RREDCoT: Segment-Level Reward Redistribution for Reasoning Models
ELLIS Unit Linz and LIT AI Lab, Institute for Machine Learning, Johannes Kepler University Linz, Austria（林茨ELLIS单元和LIT人工智能实验室，机器学习研究所，林茨约瑟夫·冯·拉格纳大学，奥地利）；Cognizant AI Lab, San Francisco, USA（认知人工智能实验室，美国旧金山）；NXAI GmbH, Linz, Austria（NXAI公司，奥地利林茨）
ThoughtFold: Folding Reasoning Chains via Introspective Preference Learning
Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；University of Science and Technology of China（中国科学技术大学）；MMLab, The Chinese University of Hong Kong（香港中文大学MMLab）
Thought Purity: A Defense Framework For Chain-of-Thought Attack
Huzhou University（湖州大学）；Zhejiang Key Laboratory of Intelligent Education Technology and Application（浙江智能教育技术与应用重点实验室）；University of Science and Technology of China（中国科学技术大学）；Alibaba Group（阿里巴巴集团）；Zhejiang University of Technology（浙江工业大学）
GFRIEND: Generative Few-shot Reward Inference through EfficieNt DPO
College of Computing and Data Science,Nanyang Technological University (NTU), Singapore（南洋理工大学计算与数据科学学院）；Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly(LILY),NTU,Singapore（南洋理工大学与UBC联合卓越老年活跃生活研究中心（LILY））
Beyond Policy Optimization: A Data Curation Flywheel for Sparse-Reward Long-Horizon Planning
Department of Mechanical Engineering, National University of Singapore（新加坡国立大学机械工程系）；Robotics Institute, Carnegie Mellon University（卡内基梅隆大学机器人研究所）；School of Computing, National University of Singapore（新加坡国立大学计算机科学学院）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；Department of Computer Science and Technology, Tsinghua University（清华大学计算机科学与技术系）
SpatialASM: Internalizing Active Mentalization for Spatial Reasoning
Shanghai AI 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）；CPII under InnoHK（InnoHK下属CPII机构）；Shanghai Innovation Institute（上海创新研究院）
Policy of Thoughts: Scaling Test-Time Training for LLM Reasoning via Online Policy Evolution
Binjiang Institute, Zhejiang University（浙江大学滨江学院）；Shanghai University of Finance and Economics（上海财经大学）；South China Normal University（华南师范大学）；LMU Munich（慕尼黑大学）；Wuhan University（武汉大学）
Fusing Bidirectional Chains of Thought and Reward Mechanisms A Method for Enhancing Question-Answering Capabilities of Large Language Models for Chinese Intangible Cultural Heritage
TCPO: Thought-Centric Preference Optimization for Effective Embodied Decision-making
Tsinghua University（清华大学）；Meituan（美团）；Northeastern University（东北大学）；Beijing University of Posts and Telecommunications（北京邮电大学）；Meta AI；Wuhan University（武汉大学）
Confidence as a Reward: Transforming LLMs into Reward Models
Fudan University（复旦大学）；Shanghai AI Laboratory（上海人工智能实验室）；Xidian University（西安电子科技大学）；The Chinese University of Hong Kong（香港中文大学）；Nanyang Technological University（南洋理工大学）
