title: Sample, Scrutinize and Scale: Effective Inference-Time Search by Scaling
    Verification
summary: Sampling-based search, a simple paradigm for utilizing test-time compute,
  involves generating multiple candidate responses and selecting the best one --
  typically by verifying 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 that uses only random sampling
  and direct self-verification results in sustained performance improvements
  that, for example, elevate the Gemini v1.5 Pro model's reasoning capabilities
  past 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 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.
paper_authors._id: 67a394a6049991184002e7f4
paper_authors.name: Eric Zhao
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-05T16:53:58.892Z
paper_authors._id: 66824dacdd73c6dd2996c166
paper_authors.avatarUrl: /avatars/7c43ccca705bfb608c8d46b68f62a89d.svg
paper_authors.fullname: Eric
paper_authors.type: user
paper_authors.user: ericzhao28
paper_authors._id: 67a394a6049991184002e7f5
paper_authors.name: Pranjal Awasthi
paper_authors._id: 67a394a6049991184002e7f6
paper_authors.name: Sreenivas Gollapudi
paper_title: Sample, Scrutinize and Scale: Effective Inference-Time Search by Scaling
    Verification
paper_summary: Sampling-based search, a simple paradigm for utilizing test-time compute,
  involves generating multiple candidate responses and selecting the best one --
  typically by verifying 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 that uses only random sampling
  and direct self-verification results in sustained performance improvements
  that, for example, elevate the Gemini v1.5 Pro model's reasoning capabilities
  past 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 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.
paper_ai_keywords: sampling-based search random sampling self-verification implicit scaling chains of thought