Reinforcement Learning with Verifiable Rewards (RLVR) has recently strengthened LLM reasoning, but its focus on final answer correctness leaves a critical gap: it does not ensure the robustness of the reasoning process itself. We adopt a simple philosophical view, robust reasoning should remain useful beyond the mind that produced it, and treat reasoning as a form of meaning transfer that must survive truncation, reinterpretation, and continuation. Building on this principle, we introduce Reinforcement Learning with Transferable Reward (RLTR), which operationalizes robustness via transfer reward that tests whether a partial reasoning prefix from one model can guide a separate model to the correct answer. This encourages LLMs to produce reasoning that is stable, interpretable, and genuinely generalizable. Our approach improves sampling consistency while improving final answer accuracy, and it reaches comparable performance in substantially fewer training steps. For example, on MATH500, RLTR achieves a +3.6%p gain in Maj@64 compared to RLVR and matches RLVR's average accuracy with roughly 2.5x fewer training steps, providing both more reliable reasoning and significantly more sample efficient.
Confidence Geometry Reveals Trace-Level Correctness in Large Language Model Reasoning
School of Computer Science and Technology, Tiangong University（天津工业大学计算机科学与技术学院）；Center for Quantum Physics and Intelligent Sciences, Department of Physics, Capital Normal University（首都师范大学量子物理与智能科学中心）
Beijing University of Posts and Telecommunications（北京邮电大学）；School of Artificial Intelligence and Data Science, University of International Business and Economics（国际经济贸易大学人工智能与数据科学学院）；Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学北京校区人工智能学院）
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）
Not All Tokens Matter: Towards Efficient LLM Reasoning via Token Significance in Reinforcement Learning
Tsinghua University（清华大学）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Peking University（北京大学）；Qwen Large Model Application Team, Alibaba（阿里云文大模型应用团队）；Microsoft（微软）；Shenzhen Key Laboratory of Ubiquitous Data Enabling, Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院 ubiquitous 数据赋能重点实验室）
Measuring Reasoning Quality in LLMs: A Multi-Dimensional Behavioral Framework
Department of Computer Engineering, Tarsus University（塔鲁斯大学计算机工程系）；School of Computing and Augmented Intelligence (SCAI), Arizona State University (ASU)（计算与增强智能学院（SCAI），亚利桑那州立大学（ASU））；HumaConn AI Consulting（HumaConn AI咨询）
Beyond Correctness: Benchmarking Multi-dimensional Code Generation for Large Language Models
University of Chinese Academy of Sciences（中国科学院大学）；Institute of Software, Chinese Academy of Sciences（中国科学院软件研究所）；Chinese Information Processing Laboratory（中文信息处理实验室）；State Key Laboratory of Computer Science（计算机科学国家重点实验室）；Key Laboratory of System Software, Chinese Academy of Sciences（中国科学院系统软件重点实验室）
Socratic-PRMBench: Benchmarking Process Reward Models with Systematic Reasoning Patterns
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Tongyi Lab, Alibaba Group（阿里云实验室）
Theory-Grounded Evaluation of Human-Like Fallacy Patterns in LLM Reasoning
The Laboratory for Human-Centered AI, Institute for Ethics in AI, Faculty of Philosophy, University of Oxford, UK（以人为中心的人工智能实验室，人工智能伦理研究所，哲学学院，牛津大学，英国）；Oxford Internet Institute, University of Oxford, UK（牛津互联网研究所，牛津大学，英国）；School of Computer Science, University of Birmingham, UK（计算机科学学院，伯明翰大学，英国）
The Spectral Geometry of Thought: Phase Transitions, Instruction Reversal, Token-Level Dynamics, and Perfect Correctness Prediction in How Transformers Reason
