Early Stopping for Language Model Reasoning

Latest papers

21 papers5 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

Same Beliefs, Different Policies: Learning Reusable Representations for Early Stopping in LLM Reasoning
Oct 21, 2026Jingwei Ni, Dmitry Abulkhanov, Vladislav Smirnov +5Early Stopping for Language Model Reasoning Early Stopping
ETH Zürich · MBZUAI · University of Zürich +1
Adaptive Stopping of Streamed Reasoning for Language Model Agents
Oct 20, 2026JunHyeok Oh, Byung-Jun LeeEarly Stopping for Language Model Reasoning Adaptive Reasoning in Language Models
Korea University
Calibration-Free Early Stopping for Chain-of-Thought Reasoning via E-processes
Oct 18, 2026Dong-Jae Lee, Gyojin Han, Junmo KimEarly Stopping for Language Model Reasoning Adaptive Reasoning in Language Models
KAIST
Settle: Learning When to Stop Reasoning
Sep 30, 2026Ryan Brown, Zihao Fu, Chris RussellEarly Stopping for Language Model Reasoning Efficient Language Model Reasoning
Oxford Internet Institute, University of Oxford · Department of Linguistics and Modern Languages, The Chinese University of Hong Kong
The Model Knows When to Stop: Training-Free Early Stopping for Long-Context Reading
Sep 28, 2026Muath Alyobi, Mohamed Eltahir, Almoayyad Abuljdail +3Early Stopping Long-Context Language Model Evaluation
King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia · Department of Computer Science, Edge Hill University, Ormskirk, England
Stable Answers, Unfinished Reasoning: Why Self-Consensus Is Not a Safe Early-Exit Signal
Sep 9, 2026Yunxiang Mo, Donghao Zhao, Hejia GengEarly Stopping LLM Reliability
HKUST · University of Oxford
</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
SABER: Stability-Aware Early Exit for LLM Reasoning via Adversarial Branch Probing
Aug 28, 2026Wanli Cheng, Haiya Xiang, Juntao Li +2Efficient Language Model Reasoning Early Stopping for Language Model Reasoning
Soochow University, China
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
UPAIR: Diagnosing Reasoning States via Uncertainty-Progress Alignment for Selective Intervention
Jul 19, 2026Cheng Yan, Zhijun Fan, Guangyang Ye +4Adaptive Inference Overthinking in Language Models
University of Science and Technology of China · AWS Agentic AI
OS-Pruner: Pruning Chains-of-Thought of Reasoning Models via Optimal Stopping
Jul 13, 2026Mohammed Ehab, Aymane El Gadarri, Vivek F. Farias +2Efficient Language Model Reasoning Sequential Decision Making
MIT · Columbia Business School
When Does Learning to Stop Help? A Cost-Aware Study of Early Exits in Reasoning Models
Jun 29, 2026Zhe Dong, Fang Qin, Manish ShahEfficient Language Model Reasoning Cost-Aware Inference
University of Maine at Presque Isle · Stanford University · Independent Researcher
Stop When Further Reasoning Won't Help: Attention-State Adaptive Generation in Reasoning Models
Jun 13, 2026Jiakai Li, Ke Qin, Rongzheng Wang +4Early Stopping for Language Model Reasoning Efficient Language Model Inference
University of Electronic Science and Technology of China, Chengdu, China · Ubiquitous Intelligence and Trusted Services Key Laboratory of Sichuan Province, Chengdu, China
ESPO: Early-Stopping Proximal Policy Optimization
May 28, 2026Zihang Li, Rui Zhou, Yingcheng Shi +8LLM Reasoning Reinforcement Learning
1Tongyi Lab , Alibaba Group · 2Peking University
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
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
TRACES: Tagging Reasoning Steps for Adaptive Cost-Efficient Early-Stopping
Apr 22, 2026Yannis Belkhiter, Seshu Tirupathi, Giulio Zizzo +1Reasoning Trace Analysis Early Stopping
IBM Research Europe · Trinity College Dublin · Dublin, Ireland +1
Early Stopping for Large Reasoning Models via Confidence Dynamics
Apr 6, 2026Parsa Hosseini, Sumit Nawathe, Mahdi Salmani +2Early Stopping for Language Model Reasoning Early Stopping
University of Maryland · University of Southern California
ROM: Real-time Overthinking Mitigation via Streaming Detection and Intervention
Mar 23, 2026Xinyan Wang, Xiaogeng Liu, Ming Pei +1Early Stopping for Language Model Reasoning Overthinking in Language Models
University of Wisconsin–Madison · Johns Hopkins University
TERMINATOR: Learning Optimal Exit Points for Early Stopping in Chain-of-Thought Reasoning
Mar 13, 2026Alliot Nagle, Jakhongir Saydaliev, Dhia Garbaya +3Early Stopping for Language Model Reasoning Efficient Language Model Inference
UT Austin · EPFL · ENS Paris-Saclay +1
Step-Tagging: Toward controlling the generation of Language Reasoning Models through step monitoring
Dec 16, 2025Yannis Belkhiter, Seshu Tirupathi, Giulio Zizzo +1Reasoning Trace Analysis Efficient Language Model Reasoning
1IBM Research Europe, Dublin, Ireland · 2Trinity College Dublin, Ireland · 3ADAPT Research Centre, Dublin, Ireland

Oct 21, 2026J/K move · Enter open · S save

Same Beliefs, Different Policies: Learning Reusable Representations for Early Stopping in LLM Reasoning

Jingwei Ni, Dmitry Abulkhanov, Vladislav Smirnov, Markus Leippold+4

ETH Zürich · MBZUAI · University of Zürich+1

Early Stopping for Language Model Reasoning Early Stopping Efficient Language Model Reasoning Adaptive Reasoning in Language Models Metacognition in Language Models Language Model Probing

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Reasoning LLMs often continue reasoning after reaching a correct answer, wasting computation and sometimes replacing it with an incorrect one. Deciding when to stop is a metareasoning problem: the model must weigh what further reasoning could achieve against its cost, given what it can already answer. Direct utility training learns a stopping representation at a chosen token price; supporting new prices can require additional controller training. We introduce BELT (BELief-based Termination), which separates outcome prediction from stopping control. It learns beliefs about current and future answers from a frozen LLM's hidden states, then fits a linear stopping policy on the frozen features. The prediction targets are independent of token price, so one probe supports different accuracy-cost trade-offs through CPU policy refitting or threshold adjustment. Averaged over five out-of-distribution (OOD) math benchmarks, BELT reduces generated tokens by 41% on Qwen3-1.7B and 51% on OLMo-3-7B-Think without loss of accuracy. The same math-trained BELT transfers to coding and agentic tasks without training on code or agentic data. In all three domains, its accuracy-cost trade-offs are competitive with or better than those of end-to-end controllers. On OLMo3, its probe has 21.2x fewer parameters than one checkpoint of the strongest baseline, OS-Pruner, and needs 55x fewer training GPU-hours than OS-Pruner's five price-specific checkpoints.