LLM Reasoning

LLM: Large Language Model

This field asks how language models can carry out multi-step inference across mathematics, coding, logic, science, and real-world tasks, and how to tell robust reasoning from pattern matching. Common approaches include chain-of-thought prompting, self-consistency and tree search at inference time, tool use and symbolic verification, and post-training with supervised traces, reinforcement learning from verifiable rewards, or process feedback. Researchers are also exploring latent and recurrent computation to reduce reliance on long written traces, alongside benchmarks that test compositionality, robustness, and long-horizon execution. A central tension is that more computation can improve accuracy but raise cost, while plausible explanations and correct final answers do not by themselves establish faithful, reliable reasoning.

Latest papers

931 papers105 papers in the last four weeks, up 27% on the four weeks before. 0.6% of all new papers.

Adapt to Thrive! Adaptive Power-Mean Policy Optimization for Improved LLM Reasoning
Apr 11, 2026Yiming Huang, Zhenbo Shi, Shuzheng Gao +3RL for Language Model Reasoning Policy Optimization
Harbin Institute of Technology, Shenzhen · The Chinese University of Hong Kong · Peng Cheng Laboratory
Free Energy-Driven Reinforcement Learning with Adaptive Advantage Shaping for Unsupervised Reasoning in LLMs
Apr 11, 2026Yiming Huang, Zhenbo Shi, Xin-Cheng Wen +4Reinforcement Learning RL for Language Model Reasoning
Harbin Institute of Technology, Shenzhen · The Chinese University of Hong Kong · Peng Cheng Laboratory
AI Achieves a Perfect LSAT Score
Apr 11, 2026Bonmu KuLLM Reasoning LLM Evaluation
Process Reward Agents for Steering Knowledge-Intensive Reasoning
Apr 10, 2026Jiwoong Sohn, Tomasz Sternal, Kenneth Styppa +2LLM Reasoning Process Reward Models
ETH Zürich, Switzerland · Heidelberg University, Germany
ASTRA: Adaptive Semantic Tree Reasoning Architecture for Complex Table Question Answering
Apr 10, 2026Xiaoke Guo, Songze Li, Zhiqiang Liu +4Table QA LLM Reasoning
Zhejiang University
SPPO: Sequence-Level PPO for Long-Horizon Reasoning Tasks
Apr 10, 2026Tianyi Wang, Yixia Li, Long Li +6RL for Language Model Reasoning Proximal Policy Optimization
Southern University of Science and Technology · Beijing University of Posts and Telecommunications · INFLY TECH +3
SUPERNOVA: Eliciting General Reasoning in LLMs with Reinforcement Learning on Natural Instructions
Apr 9, 2026Ashima Suvarna, Kendrick Phan, Mehrab Beikzadeh +2RL for Language Model Reasoning LLM Reasoning
University of California, Los Angeles
When to Trust Tools? Adaptive Tool Trust Calibration For Tool-Integrated Math Reasoning
Apr 9, 2026Ruotao Xu, Yixin Ji, Yu Luo +5Tool-Integrated Reasoning LLM Reasoning
School of Computer Science and Technology, Soochow University · Department of Foundation Model, 2012 Labs, Huawei · Harbin Institute of Technology, Shenzhen (HITSZ)
Multi-Turn Reasoning LLMs for Task Offloading in Mobile Edge Computing
Apr 8, 2026Ning Yang, Chuangxin Cheng, Haijun ZhangEdge Computing LLM Reasoning
Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China · School of Mechano-Electronic Engineering, Xidian University, Xi’an, 710071, China · School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, 100083, China
STRIDE-ED: A Strategy-Grounded Stepwise Reasoning Framework for Empathetic Dialogue Systems
Apr 8, 2026Hongru Ji, Yuyin Fan, Meng Zhao +3Emotion-Aware Dialogue Generation LLM Reasoning
School of Artificial Intelligence, OPtics and ElectroNics (iOPEN), Northwestern Polytechnical University, China · School of Artificial Intelligence and Big Data, Henan University of Technology, China · School of Computer Science, Northwestern Polytechnical University, China
GCoT-Decoding: Unlocking Deep Reasoning Paths for Universal Question Answering
Apr 8, 2026Guanran Luo, Wentao Qiu, Zhongquan Jian +2LLM Reasoning Language Model Decoding
School of Informatics, Xiamen University
Reasoning Fails Where Step Flow Breaks
Apr 8, 2026Xiaoyu Xu, Yulan Pan, Xiaosong Yuan +4LLM Reasoning Large Reasoning Models
Shanghai Jiao Tong University · Fuzhou University · Jilin University
SHAPE: Stage-aware Hierarchical Advantage via Potential Estimation for LLM Reasoning
Apr 8, 2026Zhengyang Ai, Zikang Shan, Xiaodong Ai +3LLM Reasoning Efficient Language Model Reasoning
Huawei Taylor Lab · Center for Data Science, Peking University · Shanghai University of Finance and Economics
SELFDOUBT: Uncertainty Quantification for Reasoning LLMs via the Hedge-to-Verify Ratio
Apr 7, 2026Satwik Pandey, Suresh Raghu, Shashwat PandeyLLM Uncertainty Estimation Confidence Estimation in Language Models
Independent Researcher · Zillow Group
The Master Key Hypothesis: Unlocking Cross-Model Capability Transfer via Linear Subspace Alignment
Apr 7, 2026Rishab Balasubramanian, Pin-Jie Lin, Rituraj Sharma +6Transfer Learning Activation Steering
Virginia Tech · Amazon · UNC Chapel Hill
LLM Reasoning as Trajectories: Step-Specific Representation Geometry and Correctness Signals
Apr 7, 2026Lihao Sun, Hang Dong, Bo Qiao +3LLM Reasoning Representation Geometry in Language Models
Microsoft
Towards Effective In-context Cross-domain Knowledge Transfer via Domain-invariant-neurons-based Retrieval
Apr 7, 2026Jianzhi Yan, Zhiming Li, Le Liu +6Cross-Domain Transfer Learning Retrieval-Augmented ICL
Harbin Institute of Technology, Shenzhen, China · Pengcheng Laboratory, Shenzhen, China
$π^2$ : Structure-Originated Reasoning Data Improves Long-Context Reasoning Ability of Large Language Models
Apr 6, 2026Quyet V. Do, Thinh Pham, Nguyen Nguyen +3Long-Context Reasoning in Language Models LLM Reasoning
Virginia Tech, VA 24061, USA
Are Latent Reasoning Models Easily Interpretable?
Apr 6, 2026Connor Dilgren, Sarah WiegreffeImplicit Reasoning in Language Models LLM Reasoning
Department of Computer Science, University of Maryland, College Park, MD, USA
Cog-DRIFT: Exploration on Adaptively Reformulated Instances Enables Learning from Hard Reasoning Problems
Apr 6, 2026Justin Chih-Yao Chen, Archiki Prasad, Zaid Khan +4RL for Language Model Reasoning LLM Reasoning
UNC Chapel Hill
Position: Logical Soundness is not a Reliable Criterion for Neurosymbolic Fact-Checking with LLMs
Apr 5, 2026Jason Chan, Robert Gaizauskas, Zhixue ZhaoAutomated Fact-Checking Neuro-Symbolic Reasoning
School of Computer Science, University of Sheffield, United Kingdom
Can LLMs Learn to Reason Robustly under Noisy Supervision?
Apr 5, 2026Shenzhi Yang, Guangcheng Zhu, Bowen Song +7LLM Reasoning Noisy-Label Learning
Zhejiang University · Ant Group · University of Wisconsin-Madison
When Models Know More Than They Say: Probing Analogical Reasoning in LLMs
Apr 4, 2026Hope McGovern, Caroline Craig, Thomas Lippincott +1Analogical Reasoning for Language Models LLM Reasoning
Cambridge University · Northeastern University · Athenahealth +1
TableVision: A Large-Scale Benchmark for Spatially Grounded Reasoning over Complex Hierarchical Tables
Apr 4, 2026Xiaoyu Chen, Lu Dai, Hanqing Wang +6Table Reasoning Spatial Reasoning Benchmarks
The Hong Kong University of Science and Technology (Guangzhou) · The Hong Kong University of Science and Technology · The Chinese University of Hong Kong +1
Vocabulary Dropout for Curriculum Diversity in LLM Co-Evolution
Apr 3, 2026Jacob Dineen, Aswin RRV, Zhikun Xu +1Curriculum RL RL for Language Model Reasoning
Arizona State University
TABQAWORLD: Optimizing Multimodal Reasoning for Multi-Turn Table Question Answering
Apr 3, 2026Tung Sum Thomas Kwok, Xinyu Wang, Xiaofeng Lin +7Table Reasoning Table QA
University of California, Los Angeles · SimpleWay · McGill University +2
R2-Write: Reflection and Revision for Open-Ended Writing with Deep Reasoning
Apr 3, 2026Wanlong Liu, Bo Zhang, Chenliang Li +4Open-Ended Generation LLM Reasoning
Tongyi Lab, Alibaba Group · Tsinghua University, Beijing, China
DeltaLogic: Minimal Premise Edits Reveal Belief-Revision Failures in Logical Reasoning Models
Apr 3, 2026Amit DhandaLLM Reasoning Belief Updating in LLMs
Amazon
Trivial Vocabulary Bans Improve LLM Reasoning More Than Deep Linguistic Constraints
Apr 3, 2026Rodney Jehu-AppiahLLM Reasoning
Do We Need Frontier Models to Verify Mathematical Proofs?
Apr 2, 2026Aaditya Naik, Guruprerana Shabadi, Rajeev Alur +1LLM Input Optimization Reasoning Verification
University of Pennsylvania, U.S.A.

Apr 11, 2026J/K move · Enter open · S save

Adapt to Thrive! Adaptive Power-Mean Policy Optimization for Improved LLM Reasoning

Yiming Huang, Zhenbo Shi, Shuzheng Gao, Cuiyun Gao+2

Harbin Institute of Technology, Shenzhen · The Chinese University of Hong Kong · Peng Cheng Laboratory

RL for Language Model Reasoning Policy Optimization LLM Reasoning Reinforcement Learning with Verifiable Rewards

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Reinforcement Learning with Verifiable Rewards (RLVR) is an essential paradigm that enhances the reasoning capabilities of Large Language Models (LLMs). However, existing methods typically rely on static policy optimization schemes that misalign with the model's evolving reasoning capabilities. To address this issue, we propose Adaptive Power-Mean Policy Optimization (APMPO), which comprises two main innovations: Power-Mean Policy Optimization (PMPO) and Feedback-Adaptive Clipping (FAC). Specifically, PMPO introduces a generalized power-mean objective. This enables the model to adaptively transition from the signal-amplifying behavior of the arithmetic mean to the consistency-enforcing behavior of the geometric mean. FAC adaptively adjusts clipping bounds based on real-time reward statistics to overcome the limitations of static mechanisms. Capitalizing on these innovations, APMPO improves learning dynamics and reasoning performance. Extensive experiments on nine datasets across three reasoning tasks showcase the superiority of APMPO over state-of-the-art RLVR-based baselines. For instance, APMPO boosts the average Pass@1 score on mathematical reasoning benchmarks by 3.0 points compared to GRPO when using Qwen2.5-3B-Instruct.