Parallel Test-Time Scaling with Multi-Sequence Verifiers
Parallel test-time scaling, which generates multiple candidate solutions for a single problem, is a powerful technique for improving large language model performance. However, it is hindered by two key bottlenecks: accurately selecting the correct solution from the candidate pool, and the high inference latency from generating many full solutions. We argue that both challenges are fundamentally linked to verifier calibration, as a well-calibrated verifier improves answer selection and enables early-stopping strategies to reduce latency. However, existing non-generative verifiers are limited as they score each candidate in isolation, overlooking rich contextual information across the set o...
| Authors | Yegon Kim, Seungyoo Lee, Chaeyun Jang et al. |
|---|---|
| Primary category | cs.CR |
| Published | 3 March 2026 |
| Last updated at source | 12 June 2026 |
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