Sample, Scrutinize and Scale: Effective Inference-Time Search by Scaling
  Verification
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.
67a394a6049991184002e7f4
Eric Zhao
claimed_verified
2025-02-05T16:53:58.892Z
66824dacdd73c6dd2996c166
/avatars/7c43ccca705bfb608c8d46b68f62a89d.svg
Eric
user
ericzhao28
67a394a6049991184002e7f5
Pranjal Awasthi
67a394a6049991184002e7f6
Sreenivas Gollapudi
Sample, Scrutinize and Scale: Effective Inference-Time Search by Scaling
  Verification
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.
Sampling-based search improves model performance through random sampling and self-verification, revealing implicit scaling benefits and guiding principles for enhancing verification.
sampling-based search random sampling self-verification implicit scaling chains of thought