Sample, Scrutinize and Scale: Effective Inference-Time Search by Scaling Verification

• URL: http://arxiv.org/abs/2502.01839v2
• Date: Thu, 20 Feb 2025 18:52:18 GMT
• Title: Sample, Scrutinize and Scale: Effective Inference-Time Search by Scaling Verification
• Authors: Eric Zhao, Pranjal Awasthi, Sreenivas Gollapudi,
• Abstract summary: We study the scaling trends governing sampling-based search. We find that simply scaling up a minimalist implementation of sampling-based search provides a practical inference method. We identify two useful principles for improving self-verification capabilities with test-time compute.
• Score: 35.347715518778095
• License:
• Abstract: Sampling-based search, a simple paradigm for utilizing test-time compute, involves generating multiple candidate responses and selecting the best one -- typically by having models self-verify each response for correctness. In this paper, we study the scaling trends governing sampling-based search. Among our findings is that simply scaling up a minimalist implementation of sampling-based search, using only random sampling and direct self-verification, provides a practical inference method that, for example, elevates the reasoning capabilities of Gemini v1.5 Pro above that of o1-Preview on popular benchmarks. We partially attribute the scalability of sampling-based search to a phenomenon of implicit scaling, where sampling a larger pool of responses in turn improves self-verification accuracy. We further identify two useful principles for improving self-verification capabilities with test-time compute: (1) comparing across responses provides helpful signals about the locations of errors and hallucinations, and (2) different model output styles are useful for different contexts -- chains of thought are useful for reasoning but harder to verify. We also find that, though accurate verification can be elicited, frontier models demonstrate remarkably weak out-of-box verification capabilities and introduce a benchmark to measure progress on these deficiencies.

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