Early Stopping in Large Reasoning Models: A Survey and Empirical Audit of Efficiency and Reliability

arXiv cs.AINovember 3, 2026en

arXiv:2611.00166v1 Announce Type: new Abstract: Large reasoning models can improve accuracy by generating longer reasoning traces, but some of this computation may be unnecessary. Early-stopping methods aim to stop once enough reasoning is available to answer. Evaluating these methods requires accounting for the cost of deciding when to stop and the reliability of the resulting answer. We evaluate different methods across five reasoning models on mathematical and scientific reasoning, code generation, and factuality tasks by using a shared rollout replay protocol. We introduce Target-Integrated Token Efficiency (TITE) to measure efficiency relative to the oracle across accuracy targets. We a

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