When to Vote, When to Rewrite: Disagreement-Guided Strategy Routing for Test-Time Scaling
Large Reasoning Models (LRMs) achieve strong performance on mathematical reasoning tasks but remain unreliable on challenging instances. Existing test-time scaling methods, such as repeated sampling, self-correction, and tree search, improve performance at the cost of increased computation, yet often exhibit diminishing returns on hard problems. We observe that output disagreement is strongly correlated with instance difficulty and prediction correctness, providing a useful signal for guiding instance-level strategy selection at test time. Based on this insight, we propose a training-free framework that formulates test-time scaling as an instance-level routing problem, rather than allocat...
| Authors | Zhimin Lin, Yixin Ji, Jinpeng Li et al. |
|---|---|
| Primary category | cs.AI |
| Published | 29 April 2026 |
| Last updated at source | 3 August 2026 |
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