Budget-aware Test-time Scaling via Discriminative Verification
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.
68f1ab1b6e0bef323a68fd8c
Kyle Montgomery
claimed_verified
2025-10-17T04:10:16.839Z
646c351a31968a60a0237c71
/avatars/d1b3ef3c33671d1cb097bad69e97943b.svg
Kyle Montgomery
user
kylemontgomery
68f1ab1b6e0bef323a68fd8d
Sijun Tan
68f1ab1b6e0bef323a68fd8e
Yuqi Chen
68f1ab1b6e0bef323a68fd8f
Siyuan Zhuang
68f1ab1b6e0bef323a68fd90
Tianjun Zhang
68f1ab1b6e0bef323a68fd91
Raluca Ada Popa
68f1ab1b6e0bef323a68fd92
Chenguang Wang
Budget-aware Test-time Scaling via Discriminative Verification
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.
A hybrid approach combining discriminative verification with self-consistency outperforms generative verification in test-time scaling for large language models, achieving higher accuracy within a fixed compute budget.
test-time scaling large language models generative verifiers discriminative verification self-consistency AIME2025