Statistical Early Stopping for Reasoning Models

• URL: http://arxiv.org/abs/2602.13935v1
• Date: Sun, 15 Feb 2026 00:14:53 GMT
• Title: Statistical Early Stopping for Reasoning Models
• Abstract summary: We introduce statistically principled early stopping methods that monitor uncertainty signals during generation to mitigate this issue.<n>Our results indicate that uncertainty-aware early stopping can improve both efficiency and reliability in LLM reasoning.
• Score: 32.80151987366704
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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