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[2602.13935] Statistical Early Stopping for Reasoning Models
Summary
The paper presents statistical early stopping methods for reasoning models, addressing inefficiencies in large language models (LLMs) that overthink during uncertain queries. It introduces both parametric and nonparametric approaches to enhance reasoning efficiency and reliabi...
Why It Matters
As LLMs become integral in various applications, optimizing their reasoning capabilities is crucial. This research offers methods to improve efficiency and reliability, particularly in complex reasoning tasks, which can lead to better performance in real-world applications.
Key Takeaways
- Introduces early stopping methods to reduce unnecessary reasoning steps in LLMs.
- Parametric and nonparametric approaches are proposed for different scenarios.
- Empirical evaluations show significant improvements in efficiency, especially for math reasoning tasks.
- The methods leverage uncertainty signals to enhance decision-making processes.
- Findings can inform future developments in AI reasoning and model training.
Computer Science > Artificial Intelligence arXiv:2602.13935 (cs) [Submitted on 15 Feb 2026] Title:Statistical Early Stopping for Reasoning Models Authors:Yangxinyu Xie, Tao Wang, Soham Mallick, Yan Sun, Georgy Noarov, Mengxin Yu, Tanwi Mallick, Weijie J. Su, Edgar Dobriban View a PDF of the paper titled Statistical Early Stopping for Reasoning Models, by Yangxinyu Xie and 8 other authors View PDF Abstract:While LLMs have seen substantial improvement in reasoning capabilities, they also sometimes overthink, generating unnecessary reasoning steps, particularly under uncertainty, given ill-posed or ambiguous queries. We introduce statistically principled early stopping methods that monitor uncertainty signals during generation to mitigate this issue. Our first approach is parametric: it models inter-arrival times of uncertainty keywords as a renewal process and applies sequential testing for stopping. Our second approach is nonparametric and provides finite-sample guarantees on the probability of halting too early on well-posed queries. We conduct empirical evaluations on reasoning tasks across several domains and models. Our results indicate that uncertainty-aware early stopping can improve both efficiency and reliability in LLM reasoning, and we observe especially significant gains for math reasoning. Subjects: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Machine Learning (stat.ML) Cite as: arXiv:2602.13935 [cs.AI] (or arXiv:2602.13935v1 [cs.AI] for this ver...