Adaptive Replay Refinement：在修订与重采样之间分配测试时计算

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Abstract:Test-time scaling allocates additional inference compute to refining existing solutions or generating new ones. This raises a fundamental question: Should I keep improving this solution, or stop working on it and try a completely new solution? Existing methods make this decision using fixed refinement schedules, self-verification judgments, or learned controllers, which can waste compute or rely on signals that may be incorrect or require additional training. We introduce Adaptive Replay Refinement (ARR), a training-free method that detects when a stationary revision reaches a fixed point and reallocates the released compute to a new independent sample; replay decoding treats the current solution as a draft and verifies unchanged tokens in parallel, enabling efficient fixed-point detection. Across seven models from five families and seven benchmarks, ARR matches majority voting on three of five mathematical reasoning benchmarks at equal compute, while reducing critical-path serial passes by up to 2.4 times. On MATH500, ARR achieves 84.6% accuracy with only 41% of the serial passes required by majority voting. These results demonstrate that detecting refinement exhaustion enables adaptive reallocation from revision to independent sampling, improving inference efficiency while highlighting the limits of self-revision. Code is available at this https URL.

Subjects:	Computation and Language (cs.CL); Machine Learning (cs.LG)
Cite as:	arXiv:2610.28294 [cs.CL]
	(or arXiv:2610.28294v1 [cs.CL] for this version)
	https://doi.org/10.48550/arXiv.2610.28294 arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Ahsan Bilal [view email]
[v1] Tue, 20 Oct 2026 14:41:46 UTC (516 KB)