aiqbench analysis
Settle introduces a novel method for optimizing reasoning models by training the model to detect answer stability, thereby significantly reducing token usage without substantial accuracy loss. This approach directly addresses efficiency challenges in large-scale AI deployment by extending the performance Pareto frontier for inference costs.
Settle: Learning When to Stop Reasoning
arXiv:2609.38997v1 Announce Type: new 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.
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.
Computation and Language (cs.CL)
arXiv:2609.38997 [cs.CL]
(or arXiv:2609.38997v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2609.38997 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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