Efficient Language Model Reasoning

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23 papers in the last four weeks, up 188% on the four weeks before. 0.2% of all new papers.

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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
Thinking Without Words: Efficient Latent Reasoning with Abstract Chain-of-Thought
Apr 24, 2026Keshav Ramji, Tahira Naseem, Ramón Fernandez AstudilloEfficient Language Model Reasoning Implicit Reasoning in Language Models
IBM Research AI
Large Language Models Decide Early and Explain Later
Apr 24, 2026Ayan Datta, Zhixue Zhao, Bhuvanesh Verma +3Efficient Language Model Reasoning LLM Inference Acceleration
IIIT Hyderabad · University of Sheffield, UK · Goethe University, Frankfurt am Main, Germany
Thinking with Reasoning Skills: Fewer Tokens, More Accuracy
Apr 23, 2026Guangxiang Zhao, Qilong Shi, Xusen Xiao +3Efficient Language Model Reasoning Efficient Language Model Inference
Qiyuan Tech · Tsinghua University · The University of Hong Kong +1
TRACES: Tagging Reasoning Steps for Adaptive Cost-Efficient Early-Stopping
Apr 22, 2026Yannis Belkhiter, Seshu Tirupathi, Giulio Zizzo +1LLM Inference Efficiency Early Stopping
IBM Research Europe · Trinity College Dublin · Dublin, Ireland +1
Less Languages, Less Tokens: An Efficient Unified Logic Cross-lingual Chain-of-Thought Reasoning Framework
Apr 22, 2026Chenyuan Zhang, Qiguang Chen, Xie Chen +6Cross-Lingual Reasoning in Language Models CoT Reasoning
Harbin Institute of Technology, Shenzhen · Shanghai Innovation Institute · Central South University +3
How Much Thinking is Enough? Quantifying and Understanding Redundancy in LLM Reasoning
Apr 21, 2026Zhiyuan Zhai, Xinkai You, Wenjing Yan +1LLM Inference Efficiency RL for Language Model Reasoning
Fudan University · The Chinese University of Hong Kong
Neural Garbage Collection: Learning to Forget while Learning to Reason
Apr 20, 2026Michael Y. Li, Jubayer Ibn Hamid, Emily B. Fox +1LLM Compression Memory-Efficient Optimization
Stanford University
Self-Consistency from Only Two Samples: CoT-PoT Ensembling for Efficient LLM Reasoning
Apr 19, 2026Raman Saparkhan, Majd Hawasly, Md Rizwan Parvez +1LLM Inference Efficiency Self-Consistency Decoding
Carnegie Mellon University Qatar · Qatar Computing Research Institute
Probabilistic Programs of Thought
Apr 19, 2026Poorva Garg, Renato Lui Geh, Daniel Israel +3LLM-Based Program Synthesis Efficient Language Model Reasoning
University of California Los Angeles · Allen Institute for AI
TrigReason: Trigger-Based Collaboration between Small and Large Reasoning Models
Apr 16, 2026Yi Zhao, Yajuan Peng, Cam-Tu Nguyen +4Efficient Language Model Reasoning LLM Inference Acceleration
AGI Institute, School of Computer Science, Shanghai Jiao Tong University, Shanghai, China · Shanghai Key Laboratory for Intelligent Information Processing, Fudan University · State Key Laboratory for Novel Software Technology, Nanjing University +2
Apriel-Reasoner: RL Post-Training for General-Purpose and Efficient Reasoning
Apr 2, 2026Rafael Pardinas, Ehsan Kamalloo, David Vazquez +1LLM Inference Efficiency RL for Language Model Reasoning
ServiceNow AI Research

May 12, 2026, cs.AIJ/K move · Enter open · S save

Nice Fold or Hero Call: Learning Budget-Efficient Thinking for Adaptive Reasoning

Zhaomeng Zhou, Lan Zhang, Junyang Wang, Mu Yuan+1

University of Science and Technology of China · The Chinese University of Hong Kong

RL for Language Model Reasoning Efficient Language Model Reasoning Inference-Time Compute Allocation Efficient Language Model Inference LLM Mathematical Reasoning Large Reasoning Models

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Large reasoning models (LRMs) improve problem solving through extended reasoning, but often misallocate test-time compute. Existing efficiency methods reduce cost by compressing reasoning traces or conditioning budget on perceived difficulty, yet largely overlook solvability. As a result, they may spend large budgets on queries beyond the model's capability while compressing hard-but-solvable queries that require deeper reasoning. In this work, we formulate adaptive reasoning as a computational investment under uncertainty, where budget should follow the expected return of reasoning rather than perceived difficulty alone. To instantiate this principle, we propose Budget-Efficient Thinking (BET), a two-stage framework that combines behavioral cold-start with GRPO under an investment-cost-aware reward. By aligning solve-or-fold decisions with rollout-derived solvability, BET learns three behaviors: (1) short solve, answering easy queries concisely; (2) nice fold, abstaining early when continued reasoning has near-zero expected return; and (3) hero call, preserving sufficient compute for hard-but-solvable queries. Across seven benchmarks and three base models, BET reduces reasoning tokens by ~55% on average while achieving overall performance improvements, and transfers zero-shot from mathematical reasoning to scientific QA and logical reasoning with comparable efficiency gains.

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