Reinforcement learning substantially improves reasoning in large language models, but it also tends to lengthen chain-of-thought outputs and increase computational cost. Although length-control methods have been proposed, the length-accuracy relationship they induce remains unclear. We train policies with several length-control methods on multiple base models in a controlled setup and find that, across both mathematical reasoning and code generation, accuracy is non-monotonic in output length, peaking at an intermediate value. Mode accuracy, however, continues to improve with length even in settings where sample accuracy plateaus or declines, indicating that the non-monotonic length-accuracy relationship is driven by dispersion around an increasingly correct center.
CogniLoad: A Synthetic Natural Language Reasoning Benchmark With Tunable Length, Intrinsic Difficulty, and Distractor Density
UiT - The Arctic University of Norway（UiT - 北极大学）；Integreat - Norwegian Centre for knowledge-driven machine learning（Integreat - 挪威知识驱动机器学习中心）；University of Oslo（奥斯陆大学）
The Impact of Reasoning Step Length on Large Language Models
Rutgers University（罗格斯大学）；University of Liverpool（利物浦大学）；Northwestern University（西北大学）；New Jersey Institute of Technology（新泽西理工学院）；University of Exeter（埃克塞特大学）
LEAD: Length-Efficient Adaptive and Dynamic Reasoning for Large Language Models
University of Texas at Dallas（德克萨斯大学达拉斯分校）；Emory University（埃默里大学）；Individual Researcher（独立研究员）；University of Texas at Arlington（德克萨斯大学阿灵顿分校）；University of Florida（佛罗里达大学）
Anti-Length Shift: Dynamic Outlier Truncation for Training Efficient Reasoning Models
University of Science and Technology of China（中国科学技术大学）；Xiaohongshu Inc.（小红书公司）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
Improved Logical Reasoning of Language Models via Differentiable Symbolic Programming
Carnegie Mellon University（卡内基梅隆大学）；University of Pennsylvania（宾夕法尼亚大学）；Mohamed Bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Petuum Inc.（Petuum公司）
ReasoningLM: Enabling Structural Subgraph Reasoning in Pre-trained Language Models for Question Answering over Knowledge Graph
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学高瓴人工智能学院）；Beijing Key Laboratory of Big Data Management and Analysis Methods（大数据管理与分析方法北京市重点实验室）；School of Information, Renmin University of China（中国人民大学信息学院）；Alibaba Group（阿里巴巴集团）
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）
Great Truths are Always Simple: A Rather Simple Knowledge Encoder for Enhancing the Commonsense Reasoning Capacity of Pre-Trained Models
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学高瓴人工智能学院）；School of Information, Renmin University of China（中国人民大学信息学院）；Beijing Academy of Artificial Intelligence（北京智源人工智能研究院）
InfoDensity: Rewarding Information-Dense Traces for Efficient Reasoning
Institute for Infocomm Research (I 2 R), A*STAR, Singapore（信息与通信研究机构（I 2 R），A*STAR，新加坡）；Centre for Frontier AI Research (CFAR), A*STAR, Singapore（前沿人工智能研究中心（CFAR），A*STAR，新加坡）
Train Long, Think Short: Curriculum Learning for Efficient Reasoning
King Abdullah University of Science and Technology (KAUST), Saudi Arabia（卡布尔大学科学与技术大学）；Massachusetts Institute of Technology (MIT), Cambridge, MA, USA（麻省理工学院）；Princeton University, Princeton, NJ, USA（普林斯顿大学）
SelfBudgeter: Adaptive Token Allocation for Efficient LLM Reasoning
State Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University（多媒体信息处理国家重点实验室，计算机学院，北京大学）；BandAI, Bytedance（字节跳动BandAI）
On the Step Length Confounding in LLM Reasoning Data Selection
College of Computer Science and Technology, Jilin University（吉林大学计算机科学与技术学院）；Key Laboratory of Symbolic Computation and Knowledge Engineering, MoE, Jilin University（吉林大学符号计算与知识工程教育部重点实验室）；Alibaba Cloud Computing（阿里云）；School of Artificial Intelligence, Jilin University（吉林大学人工智能学院）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；Department of Mathematics, University of Michigan（密歇根大学数学系）；RIKEN Center for Advanced Intelligence Project（理化学研究所先进智能研究中心）
ReCUT: Balancing Reasoning Length and Accuracy in LLMs via Stepwise Trails and Preference Optimization
School of Computer Science and Engineering, Northeastern University, China（东北大学计算机科学与工程学院）；Department of Computer Science and Technology, Institute for AI, Tsinghua University, China（清华大学计算机科学与技术系）；Beijing National Research Center for Information Science and Technology, China（北京信息科学与技术国家研究中心）；Institute of Big Data Science and Industry/School of Computer and Information Technology, Shanxi University, China（山西大学大数据科学与产业研究院/计算机与信息学院）
