Efficient Language Model Reasoning

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This field asks how language models can solve tasks such as math, coding, science questions, and multi-hop retrieval without spending unnecessary computation. Common approaches compress or prune chain-of-thought, stop generation when answers stabilize, and adapt reasoning budgets to task difficulty; others use reinforcement learning, distillation, search, parallel sampling, or smaller-model routing. Newer work also explores latent reasoning and memory or KV-cache management to reduce the cost of generating and retaining explicit thoughts. The central tension is that extra reasoning can help on difficult problems but may waste resources, introduce errors, or even hurt accuracy, so methods are judged by both quality and cost on benchmarks such as GSM8K, MATH, AIME, and GPQA.

Latest papers

373 papers89 papers in the last four weeks, up 218% on the four weeks before. 0.5% of all new papers.

Learning to Reason Efficiently with A* Post-Training
May 23, 2026Andreas Opedal, Francesco Ignazio Re, Abulhair Saparov +3Process Reward Models RL for Language Model Reasoning
αETH Zürich · γPurdue University · βMPI for Intelligent Systems, Tübingen
HyperGuide: Hyperbolic Guidance for Efficient Multi-Step Reasoning in Large Language Models
May 22, 2026Yuyu Liu, Haotian Xu, Yanan He +3LLM Fine-Tuning Efficient Language Model Reasoning
Department of Computer Science Stony Brook University · Department of Applied Mathematics and Statistics Stony Brook University · Department of Computer Science Yale University +2
PathCal: State-Aware Reflection-Marker Calibration for Efficient Reasoning
May 21, 2026Lingyu Jiang, Zirui Li, Lingyu Xu +7Efficient Language Model Reasoning Efficient Language Model Inference
Tohoku University · Texas A&M University · Worcester Polytechnic Institute
CLORE: Content-Level Optimization for Reasoning Efficiency
May 21, 2026Yuyang Wu, Qiyao Xue, Guanxing Lu +4RL for Language Model Reasoning Efficient Language Model Reasoning
Carnegie Mellon University · University of North Carolina, Chapel Hill · University of Pittsburgh +1
When Should LLMs Reason? Adaptive Inference Routing via Entropy Dynamics
May 20, 2026Wei Xia, Haoqing Wang, Zhi-Hong Deng +1Language Model Decoding CoT Reasoning
State Key Laboratory of General Artificial Intelligence, School of Intelligence Science and Technology, Peking University · Samsung Research, Beijing, China
Stop When Reasoning Converges: Semantic-Preserving Early Exit for Reasoning Models
May 17, 2026Dehai Min, Giovanni Vaccarino, Huiyi Chen +3Large Reasoning Models CoT Reasoning
University of Illinois Chicago · Politecnico di Milano · University of Illinois Urbana-Champaign +1
Reasoning Can Be Restored by Correcting a Few Decision Tokens
May 16, 2026Changshuo Shen, Leheng Sheng, Yuxin Chen +2Efficient Language Model Reasoning Inference-Time Compute Allocation
University of Science and Technology of China, Hefei, China · National University of Singapore, Singapore
CAPS: Cascaded Adaptive Pairwise Selection for Efficient Parallel Reasoning
May 15, 2026Fangzhou Lin, Shuo Xing, Peiran Li +6LLM Reasoning Pairwise Comparison
Texas A&M University · Georgia Institute of Technology · Tohoku University +1
Distribution Corrected Offline Data Distillation for Large Language Models
May 13, 2026Yumeng Zhang, Zhengbang Yang, Yevin Nikhel Goonatilake +1CoT Distillation Online Knowledge Distillation
George Mason University, Fairfax, VA, USA
Enhanced and Efficient Reasoning in Large Learning Models
May 13, 2026Leslie G. ValiantRepresentation Learning World Models
John A. Paulson School of Engineering and Applied Sciences Harvard University, Boston, MA 02134
STOP: Structured On-Policy Pruning of Long-Form Reasoning in Low-Data Regimes
May 13, 2026Chenjun Xu, Zhennan Zhou, Zhan Su +3LLM Pruning LLM Fine-Tuning
University of Washington · University of Montreal
Nice Fold or Hero Call: Learning Budget-Efficient Thinking for Adaptive Reasoning
May 12, 2026Zhaomeng Zhou, Lan Zhang, Junyang Wang +2RL for Language Model Reasoning Efficient Language Model Reasoning
University of Science and Technology of China · The Chinese University of Hong Kong
LEAD: Length-Efficient Adaptive and Dynamic Reasoning for Large Language Models
May 10, 2026Songtao Wei, Yi Li, Zhikai Li +7RL for Language Model Reasoning Efficient Language Model Reasoning
University of Texas at Dallas · Emory University · University of Texas at Arlington +2
Efficient LLM Reasoning via Variational Posterior Guidance with Efficiency Awareness
May 10, 2026Zizhao Chen, Yuying Li, Siting Lin +1LLM Inference Efficiency Efficient Language Model Reasoning
Guangdong University of Foreign Studies, Guangzhou, China.
Hint Tuning: Less Data Makes Better Reasoners
May 9, 2026Siqi Fan, Minghao Li, Xiaoqian Ma +6Overthinking in Language Models LLM Fine-Tuning
University of Electronic Science and Technology of China · Xiaohongshu Inc. · National University of Singapore
The Coupling Tax: How Shared Token Budgets Undermine Visible Chain-of-Thought Under Fixed Output Limits
May 8, 2026Wenhua Nie, Junlin Liu, Jianan Wu +5CoT Evaluation CoT Reasoning
National Taiwan University
ExpThink: Experience-Guided Reinforcement Learning for Adaptive Chain-of-Thought Compression
May 8, 2026Tingcheng Bian, Yuzhe Zhang, Jing Jin +5Reward Shaping RL for Language Model Reasoning
1Baidu Inc. · 2Shenzhen University · 3Peking University +2
Confidence-Aware Alignment Makes Reasoning LLMs More Reliable
May 8, 2026Kejia Chen, Jiawen Zhang, Yihong Wu +5LLM Alignment Direct Preference Optimization
Zhejiang University · Université de Montréal · Sun Yat-sen University +1
LaTER: Efficient Test-Time Reasoning via Latent Exploration and Explicit Verification
May 8, 2026Xuan Li, Yining Wang, Yuchen Liu +7CoT Reasoning Efficient Language Model Reasoning
University of Science and Technology of China · Unisound AI Technology Co., Ltd · MAIS, Institute of Automation, Chinese Academy of Sciences
Rethinking RL for LLM Reasoning: It's Sparse Policy Selection, Not Capability Learning
May 7, 2026Ömer Faruk Akgül, Rajgopal Kannan, Willie Neiswanger +1RL for Language Model Reasoning Efficient Language Model Reasoning
University of Southern California · 2DEVCOM ARL
Policy-Guided Stepwise Model Routing for Cost-Effective Reasoning
May 7, 2026Wenwen Si, Insup Lee, Osbert BastaniCost-Aware Inference Efficient Language Model Reasoning
Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA 19104, USA
ZAYA1-8B Technical Report
May 6, 2026Robert Washbourne, Rishi Iyer, Tomas Figliolia +15Mixture-of-Experts Language Models RL for Language Model Reasoning
Zyphra San Francisco, CA
Distilling Long-CoT Reasoning through Collaborative Step-wise Multi-Teacher Decoding
May 4, 2026Taewon Yun, Jisu Shin, Jeonghwan Choi +2CoT Distillation Efficient Language Model Reasoning
KAIST · UNIST
Multi-Agent Reasoning Improves Compute Efficiency: Pareto-Optimal Test-Time Scaling
May 2, 2026Florian Valentin Wunderlich, Lars Benedikt Kaesberg, Jan Philip Wahle +2Self-Consistency Decoding Cost-Aware Inference
University of Göttingen, Germany
When Less is Enough: Efficient Inference via Collaborative Reasoning
May 1, 2026Yilei Chen, Sharut Gupta, Yannis Paschalidis +2Efficient Language Model Reasoning Efficient Language Model Inference
Boston University · MIT CSAIL · Biohub
Select to Think: Unlocking SLM Potential with Local Sufficiency
Apr 29, 2026Wenxuan Ye, Yangyang Zhang, Xueli An +2Small Language Models Efficient Language Model Reasoning
1Technical University of Munich · 3Ludwig Maximilian University of Munich · 2Huawei Technologies +2
Shorthand for Thought: Compressing LLM Reasoning via Entropy-Guided Supertokens
Apr 29, 2026Zhenyu Zhao, Sander Land, Daniel M. Bikel +1LLM Compression LLM Interpretability
Writer, Inc.
Dual-Track CoT: Budget-Aware Stepwise Guidance for Small LMs
Apr 27, 2026Sagnik Chatterjee, Atharva Patil, Sricharan RameshCoT Reasoning Small Language Models
Stabilizing Efficient Reasoning with Step-Level Advantage Selection
Apr 27, 2026Han Wang, Xiaodong Yu, Jialian Wu +4RL for Language Model Reasoning Efficient Language Model Reasoning
UNC Chapel Hill · Advanced Micro Devices, Inc.
Tandem: Riding Together with Large and Small Language Models for Efficient Reasoning
Apr 26, 2026Zichuan Fu, Xian Wu, Guojing Li +7Efficient Language Model Reasoning LLM Inference Acceleration
City University of Hong Kong · Tencent Jarvis Lab · Renmin University of China +1

May 23, 2026J/K move · Enter open · S save

Learning to Reason Efficiently with A* Post-Training

Andreas Opedal, Francesco Ignazio Re, Abulhair Saparov, Mrinmaya Sachan+2

αETH Zürich · γPurdue University · βMPI for Intelligent Systems, Tübingen

Process Reward Models RL for Language Model Reasoning Efficient Language Model Reasoning LLM Reasoning Structured Reasoning in Language Models

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Many applications of large language models (LLMs) require deductive reasoning, yet models frequently produce incorrect or redundant inference steps. We frame natural language inference as a search problem where the final answer is the valid proof itself, requiring a reasoning procedure in which intermediate inferences are correct. Specifically, we investigate whether LLMs can learn to generate correct and efficient proofs with guidance from A* search -- an algorithm that guarantees an optimally efficient path to a goal. We explore two training techniques: supervised fine-tuning on execution traces from A* and reinforcement learning with A*-informed process reward models. Empirically, we find that Llama-3.2 models in the 1B--3B range benefit substantially from A* post training, going from near-zero accuracy to outperforming DeepSeek-V3.2 -- a much larger model. Our analysis uncovers a trade-off: while simple correctness rewards maximize accuracy, A*-informed signals strike a balance between accuracy and efficiency. Furthermore, we find that on larger search spaces, models trained with imperfect heuristics exhibit superior accuracy. Our results demonstrate a promising direction towards reasoning guided by principles derived from classical search algorithms.