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arXiv · 2602.13935

Statistical Early Stopping for Reasoning Models

Yangxinyu Xie·Tao Wang·Soham Mallick·Yan Sun·Georgy Noarov·Mengxin Yu·Tanwi Mallick·Edgar Dobriban
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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.

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Yangxinyu Xie, Tao Wang, Soham Mallick, Yan Sun, Georgy Noarov, Mengxin Yu, Tanwi Mallick, Edgar Dobriban. 2026-07-24. Statistical Early Stopping for Reasoning Models. https://arxiv.org/abs/2602.13935

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