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.