Ahsan Bilal, Muhammad Ahmed Mohsin, Muhammad Umer, Ayesha Mohsin, Sarmad Bilal, Dean F. Hougen · 2026 · arXiv
DOI: 10.48550/arXiv.2610.28294 · open-access PDF
测试时间的扩展将额外的推断计算分配给改进现有解决方案或生成新解决方案,这引发了一个根本问题:我是否应该继续改进这个解决方案,或者停止工作,并尝试一个全新的解决方案?现有的方法使用固定改进时间表,自我验证判断或学习控制器来做出这个决定,这些方法可以浪费计算或依靠可能不正确或需要额外培训的信号。
Machine translation (Meta M2M100); the English original follows.
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
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