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

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 (REFlective-Redundancy for Adaptive INference), 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-tostop as a new and practical axis of test-time scaling -enabling models to reason not just more, but just enough.