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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Giving Credit Where It's Due: Redundancy-Aware Learning for Efficient Reasoning
Sep 22, 2026Yuqing Zhou, Hong Wang, Manqing Mao +8RL for Language Model Reasoning Step-Level Credit Assignment in RL
George Mason University · Amazon, Inc
Capable yet Parsimonious: Extracting and Characterizing Hidden Chain-of-Thought in Frontier Models
Sep 22, 2026Xiaoyu Luo, Tao Ren, Wenrui Yu +3LLM Interpretability CoT Reasoning
Department of Computer Science Aalborg University, Copenhagen, Denmark · Department of Electronic Systems Aalborg University, Copenhagen, Denmark · Seafill
Rollout Efficiency in Reinforcement Learning for Reasoning Large Language Models: A Taxonomy and Future Directions
Sep 21, 2026Niloofar Gholipour, Marcos Assuncao, Gursimran Singh +8LLM Inference Efficiency Reinforcement Learning
École de technologie supérieure, Univ. of Québec, Canada · Huawei Technologies, Canada · The Univ. of Melbourne, Australia +1
When2Think: Learning When and How Much to Reason
Sep 17, 2026Jaejun Shim, HyunJin Kim, Young Jin Kim +1Overthinking in Language Models RL for Language Model Reasoning
Sungkyunkwan University · Microsoft
Efficient Reasoning Distillation: Small Video-Language Models via Synthetic CoT and Difficulty-Aware Fine-Tuning
Sep 14, 2026Mantek Singh, Jeshwanth Challagundla, Siddharth Raina +1CoT Distillation VLM Distillation
Liverpool John Moores University, Liverpool, England · Carnegie Mellon University, Pittsburgh, USA · Meta, Sunnyvale, USA +1
Self-Orchestrating Language Models: Leveraging Semantic Dependence for Efficient Inference
Sep 13, 2026Tian JinLong-Context Language Model Inference Efficient Language Model Reasoning
Department of Electrical Engineering and Computer Science, MASSACHUSETTS INSTITUTE OF TECHNOLOGY
Lightning Weave: Improving the Accuracy-Efficiency Frontier of Reasoning Models through Capability Composition
Sep 13, 2026Yecheng Wu, Song Han, Han CaiEfficient Language Model Reasoning Language Model Distillation
Massachusetts Institute of Technology · NVIDIA
RetroThinker: Enabling Retrospective Thinking in Speech LLMs
Sep 12, 2026Yi-Jen Shih, Puyuan Peng, Abdelrahman Mohamed +1LLM Self-Correction Efficient Language Model Reasoning
The University of Texas at Austin · FAIR, Meta Superintelligence Labs
A*-Thought-V2: Efficient Latent Reasoning via Geometric Dynamics of LLM
Sep 7, 2026Xiaoang Xu, Siyuan Liu, Shuo Wang +13Efficient Language Model Reasoning Continuous Latent Reasoning
Beijing University of Posts and Telecommunications · OpenBMB · The Hong Kong Polytechnic University +2
</think> Doesn't Stop Reasoning: Analysis of Spurious CoT Termination
Sep 3, 2026Seunghee Koh, Sungjae Choi, Minchan Kwon +2CoT Reasoning Efficient Language Model Reasoning
Korea Advanced Institute of Science and Technology, South Korea
Random Attention: Rethinking KV Cache Eviction for Efficient Reasoning
Sep 3, 2026Heng Wang, Jielin Qiu, Wenting Zhao +8KV Caching Memory-Efficient Optimization
Salesforce AI Research · University of Illinois Urbana-Champaign
Latent Recurrent Thoughts: Recurrent Refinement of Proposed Latents for Reasoning with Frozen LLMs
Sep 1, 2026Zhaoliang Chen, Jie FuEfficient Language Model Reasoning Continuous Latent Reasoning
Emory University · IQuest Research
A Model with No Head and Many Thoughts
Aug 31, 2026Nikita Koriagin, Yaroslav Aksenov, George Bredis +3Efficient Language Model Reasoning LLM Inference Acceleration
1Yandex · 2T-Tech
Every Token Leaves a Ripple in the Stream of Thought: Eliciting Model-Internal Token Saliency for Chain-of-Thought Compression
Aug 31, 2026Tianyi Zhao, Yinhan He, Wendy Zheng +2LLM Compression Efficient Language Model Reasoning
University of Virginia
HSRM: Hidden-State Reward Models for Test-Time Verification
Aug 31, 2026Xianzhi Li, Xiaodan ZhuLLM Answer Verification Efficient Language Model Reasoning
Department of Electrical and Computer Engineering & Ingenuity Labs Research Institute Queen’s University
ERR+: Sequential Entropy Resolution for Efficient and Decisive LLM Reasoning
Aug 28, 2026Xin Jiang, Minhao Wang, Wen Wu +4Reward Shaping RL for Language Model Reasoning
School of Computer Science and Technology, East China Normal University, Shanghai 200241, China · State Key Laboratory of Estuarine and Coastal Research, School of Computer Science and Technology, East China Normal University, Shanghai 200241, China
BDH-CQ: In-Context Learning with Recurrent Latent Reasoning
Aug 10, 2026Björn Engdahl, Adrian Kosowski, Jan Chorowski +6Cost-Aware Inference Efficient Language Model Reasoning
New York University
Thought-Level Beam Search for Reasoning
Aug 8, 2026Lijie Yang, Hongyin Luo, Jiawei Zhao +2Inference-Time Search Efficient Language Model Reasoning
Princeton University · MIT CSAIL · Meta AI
Reason Wide, Not Deep: Amortizing the Reasoning Premium into Distilled Skills
Aug 8, 2026Agamdeep Singh, Srishti Gautam, Priyanshu Gupta +3Efficient Language Model Reasoning LLM Agent Skill Learning
Microsoft
Fewer Tokens, Smaller Cache: Reward-Coordinated Efficient Reasoning
Aug 5, 2026Qiyuan Zhu, Dezhi Li, Pengyu Cheng +8Overthinking in Language Models KV Caching
1HKUST · 2XJTU · 3TSE +1
Interpretable Adaptive Sampling for LLM Test-Time Scaling
Aug 4, 2026Mobina Kashaniyan, Ali JannesariLLM Inference Efficiency Test-Time Scaling
Iowa State University Ames, IA, USA
Shorter Reasoning, Earlier Answers? An Evaluation of Reasoning Interfaces
Aug 4, 2026Francesca Carlon, Vincent Ginis, Andres AlgabaLLM Evaluation Overthinking in Language Models
Data Analytics Lab, Vrije Universiteit Brussel, Pleinlaan 5, 1050 Brussels, Belgium · imec-SMIT, Vrije Universiteit Brussel, Pleinlaan 9, 1050 Brussels, Belgium · School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138, USA
BLADE: Boundary-Expanded and Layer-Adaptive Dynamic Exit for Efficient LLM Reasoning
Jul 31, 2026Keshu Fu, Keqin Peng, Jun Bai +6Efficient Language Model Reasoning Efficient Language Model Inference
School of Computer Science and Engineering, Beihang University · Beijing Institute for General Artificial Intelligence, BIGAI · Academy for Advanced Interdisciplinary Studies, Peking University +1
QLPO: Quadrant-weighted Sampling for Length-aware Policy Optimization
Jul 23, 2026Siwei Chen, Siqi Chen, Xupeng Miao +1Overthinking in Language Models RL for Language Model Reasoning
School of Computer Science, Peking University · Beijing Key Laboratory of Software and Hardware Cooperative Artificial Intelligence Systems · Department of Electronic Engineering, Tsinghua University +1
PyroDash: Cost-Efficient Token-Level Small-Large Language Model Collaborative Inference
Jul 22, 2026Niqi Lyu, Pengtao Shi, Wei Qiu +4RL for Language Model Reasoning Cost-Aware Inference
Pyromind Dynamics Inc.
EvoThink: Evolving Thinking in Large Reasoning Models via Self-Pruning and Aha-Moment Preference Optimization
Jul 22, 2026Xinbang Dai, Zheyu Xin, Huikang Hu +7Overthinking in Language Models Self-Training for Language Models
Southeast University · Noah’s Ark Lab
Efficient Chain-of-Modality Reasoning via Progressive Compression for Spoken Language Models
Jul 22, 2026Pengchao Feng, Chao-Hong Tan, Qian Chen +3Efficient Language Model Reasoning Speech Language Models
Shanghai Jiao Tong University · Shanghai Innovation Institute · Shanghai, China +2
The Price of Reasoning: Cost-Quality Tradeoffs in Reinforcement Learning for Neural Machine Translation
Jul 21, 2026Michael Jungo, Aixiu AnRL for Language Models RL for Language Model Reasoning
AIBEX, University of Fribourg Fribourg, Switzerland · iCoSys, University of Applied Sciences and Arts Western Switzerland Fribourg, Switzerland
MADA-RL: Multi-Agent Debate-Aware Reinforcement Learning for Parameter-Efficient Reasoning in Compact Models
Jul 20, 2026Martino M. L. Pulici, Cuong Xuan Chu, Evgeny Kharlamov +3RL for Language Model Reasoning Multi-Agent Debate
Bosch Center for Artificial Intelligence, Germany · LMU Munich, Germany · University of Oslo, Norway +2
Better Starts, Better Ends: Bootstrapped Iterative Self-Reasoning Distillation for Compressed Reasoning
Jul 17, 2026Leichao Dong, Dongxu Zhang, Yiding Sun +4CoT Distillation Efficient Language Model Reasoning
1Xi’an Jiaotong University · 2Peking University

Sep 22, 2026, cs.CLJ/K move · Enter open · S save

Giving Credit Where It's Due: Redundancy-Aware Learning for Efficient Reasoning

Yuqing Zhou, Hong Wang, Manqing Mao, Zhuoer Wang+7

George Mason University · Amazon, Inc

RL for Language Model Reasoning Step-Level Credit Assignment in RL Efficient Language Model Reasoning LLM Mathematical Reasoning

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Large reasoning models can produce correct yet unnecessarily long reasoning traces. Existing methods improve reasoning efficiency with trajectory-level objectives or local token- and step-level signals, but rarely model inter-step semantic dependencies. This limits their ability to distinguish redundant steps from those that support later deductions, making it harder to shorten reasoning without sacrificing accuracy. We introduce RECAP (REdundancy-aware Credit Assignment via Propagation), which addresses this limitation by assigning credit where it is due based on both a step's downstream role in the reasoning structure and its contribution to solving the problem correctly. We define structural responsibility to capture the step's downstream role by measuring how strongly later reasoning depends on it, using credit propagated backward from the final-answer node through an outcome-independent, LLM-annotated semantic dependency graph. However, a step can have high structural responsibility yet steer the reasoning away from the correct solution. RECAP therefore introduces step efficacy to measure answer-directed progress through changes in gold-answer log-likelihood as each step is added. Together, these signals reshape rollout-level GRPO advantages into step-specific updates. RECAP requires neither a separately trained process reward model nor preconstructed concise trajectories. Across two 7B models and four mathematical reasoning benchmarks, RECAP improves the accuracy-efficiency trade-off. On Qwen2.5-Math-7B, it improves pass@1 by 2.0-3.7 percentage points while reducing reasoning tokens by 8%-31% relative to GRPO across all four benchmarks. Analysis suggests these savings reflect fewer reasoning operations and less dead-end reasoning, rather than more compact expression.

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