Settle: Learning When to Stop Reasoning
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Computer Science > Computation and Language
arXiv:2609.38997 (cs)
[Submitted on 30 Sep 2026]
Title:Settle: Learning When to Stop Reasoning
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Abstract:Reasoning models often continue generating after their answers have settled. Settle learns when to stop from answer stability in completed traces. It trains the existing end-of-reasoning token while keeping other predictions close to the base model, and requires only ordinary decoding at inference. On MATH-500 with Qwen3-4B, Settle reduces token count by 40% with a 0.5-percentage-point decrease in accuracy. It gains 6.16 percentage points over supervised fine-tuning on the same traces shortened at their first stable answer, at nearly identical token counts. Its stopping score predicts whether a correct answer will remain correct. Settle extends the accuracy-token-count Pareto frontier of the evaluated stopping methods.
| Comments: | 30 pages, 4 figures |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2609.38997 [cs.CL] |
| (or arXiv:2609.38997v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2609.38997
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled Settle: Learning When to Stop Reasoning, by Ryan Brown and 2 other authors
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