Stop When Enough: Adaptive Early-Stopping for Chain-of-Thought Reasoning

• URL: http://arxiv.org/abs/2510.10103v1
• Date: Sat, 11 Oct 2025 08:30:00 GMT
• Title: Stop When Enough: Adaptive Early-Stopping for Chain-of-Thought Reasoning
• Authors: Renliang Sun, Wei Cheng, Dawei Li, Haifeng Chen, Wei Wang,
• Abstract summary: REFRAIN is a training-free framework that determines when to stop reasoning to mitigate overthinking.<n> REFRAIN reduces token usage by 20-55% while maintaining or improving accuracy compared to standard CoT prompting.
• Score: 46.106795445750855
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Chain-of-Thought (CoT) reasoning has driven recent gains of large language models (LLMs) on reasoning-intensive tasks by externalizing intermediate steps. However, excessive or redundant reasoning -- so-called overthinking -- can increase inference costs and lead LLMs toward incorrect conclusions. In this paper, we present REFRAIN ($\underline{REF}$lective-$\underline{R}$edundancy for $\underline{A}$daptive $\underline{IN}$ference), a training-free framework that adaptively determines when to stop reasoning to mitigate overthinking. REFRAIN integrates a two-stage stop discriminator to identify reflective yet redundant reasoning and a sliding-window Upper Confidence Bound (SW-UCB) multi-armed bandit controller to dynamically adjust stopping thresholds according to problem difficulty without supervision or fine-tuning. Across four representative benchmarks and two model families, REFRAIN reduces token usage by 20-55% while maintaining or improving accuracy compared to standard CoT prompting. Extensive ablation and robustness analyses demonstrate its stability across models, scorers, and prompt variations. In summary, our findings highlight when-to-stop as a new and practical axis of test-time scaling -- enabling models to reason not just more, but just enough.

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