Mahdi Salmani, Adel Javanmard, Devansh Gupta, Meisam Razaviyayn · 2026 · arXiv
DOI: 10.48550/arXiv.2610.31667 · open-access PDF
Test-time scaling has emerged as a key inference strategy for reasoning models, where a model generates a large number of candidate solutions in the hope of finding at least one correct solution. Running all candidate solutions to completion can be computationally demanding, so recent work prunes the candidate pool after generating only a short prefix, committing further computation only to the surviving candidates. Existing pruning strategies divide broadly into two categories: Quality-based methods assign a score to each partial solution and discard those predicted to ultimately lead to an i
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