Budget-aware Test-time Scaling via Discriminative Verification
Authors: name: Kyle Montgomery, name: Sijun Tan, name: Yuqi Chen, name: Siyuan Zhuang, name: Tianjun Zhang, name: Raluca Ada Popa, name: Chenguang Wang
Organization: WangLab

Test-time scaling is a powerful strategy for boosting the performance of
large language models on complex reasoning tasks. While state-of-the-art
approaches often employ generative verifiers to select the best solution from a
pool of candidates, this method incurs prohibitive computational costs,
limiting its practicality. In this work, we shift the focus to a more
budget-aware paradigm: discriminative verification. We conduct a thorough
empirical analysis and demonstrate that while discriminative verifiers may
underperform in isolation, combining them with self-consistency in a hybrid
approach creates a powerful and efficient test-time scaling mechanism. Notably,
under a fixed compute budget, this hybrid approach surpasses state-of-the-art
generative verification by a significant margin: achieving up to 15.3\% higher
accuracy on AIME2025. Our findings establish that for practical, real-world
applications, budget-aware scaling with discriminative verifiers is not only a
"free" upgrade over self-consistency, but also a more effective and efficient
alternative to costly generative techniques. Code is available at
https://github.com/wang-research-lab/verification.