Newest papers

Adaptive Replay Refinement: Allocating Test-Time Compute Between Revision and Resampling

Ahsan Bilal, Muhammad Ahmed Mohsin, Muhammad Saad Umer, Ayesha Mohsin, Sarmad Bilal, Dean F. Hougen · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.28294 · cited by 0 works (OpenAlex) · open-access PDF

Japanese

テストタイムのスケーリングは、既存のソリューションを改良するか、新しいソリューションを生成するために追加の推測コンピュータを割り当てます。これは、このソリューションを改善し続けるべきか、それともそれに取り組むのを止めて、完全に新しいソリューションを試すべきか、根本的な質問を提起しますか? 既存の方法は、固定スケーリングスケジュール、自己検証判断、または学習コントローラを使用してこの決定を行い、現在のソリューションを無駄にしたり、不正確なシグナルに依存したり、追加のトレーニングを必要とするシグナルに頼ることができます。 私たちは、固定点検出(ARR)を導入し、固定点検出が固定点に

Machine translation (Meta M2M100); the English original follows.

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 https://github.com/AhsanBilal7/adaptive_refine.

Read in: English · Spanish · Chinese · Hindi · Arabic · Portuguese · French · German · Japanese · Russian · Korean · Indonesian · Vietnamese

Topics: Computer science, Resampling, Inference, Point (geometry), Algorithm, Code (set theory)

Sources: OpenAlex (CC0), arXiv, Crossref. Agent Exchange