title: T1: Tool-integrated Self-verification for Test-time Compute Scaling in
    Small Language Models
summary: Recent studies have demonstrated that test-time compute scaling effectively
  improves the performance of small language models (sLMs). However, prior
  research has mainly examined test-time compute scaling with an additional
  larger model as a verifier, leaving self-verification by sLMs underexplored. In
  this work, we investigate whether sLMs can reliably self-verify their outputs
  under test-time scaling. We find that even with knowledge distillation from
  larger verifiers, sLMs struggle with verification tasks requiring memorization,
  such as numerical calculations and fact-checking. To address this limitation,
  we propose Tool-integrated self-verification (T1), which delegates
  memorization-heavy verification steps to external tools, such as a code
  interpreter. Our theoretical analysis shows that tool integration reduces
  memorization demands and improves test-time scaling performance. Experiments on
  the MATH benchmark demonstrate that, with T1, a Llama-3.2 1B model under
  test-time scaling outperforms the significantly larger Llama-3.1 8B model.
  Moreover, T1 generalizes effectively to both mathematical (MATH500) and
  multi-domain knowledge-intensive tasks (MMLU-Pro). Our findings highlight the
  potential of tool integration to substantially improve the self-verification
  abilities of sLMs.
paper_authors._id: 67f48eff463c4d1c4ae5284b
paper_authors.name: Minki Kang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-08T09:12:42.979Z
paper_authors._id: 64b74920fe6a108d03fed767
paper_authors.avatarUrl: /avatars/a2c05b809c36fa5fab8e1a43b3e67051.svg
paper_authors.fullname: Minki Kang
paper_authors.type: user
paper_authors.user: Nardien
paper_authors._id: 67f48eff463c4d1c4ae5284c
paper_authors.name: Jongwon Jeong
paper_authors._id: 67f48eff463c4d1c4ae5284d
paper_authors.name: Jaewoong Cho
paper_title: T1: Tool-integrated Self-verification for Test-time Compute Scaling in
    Small Language Models
paper_summary: Recent studies have demonstrated that test-time compute scaling effectively
  improves the performance of small language models (sLMs). However, prior
  research has mainly examined test-time compute scaling with an additional
  larger model as a verifier, leaving self-verification by sLMs underexplored. In
  this work, we investigate whether sLMs can reliably self-verify their outputs
  under test-time scaling. We find that even with knowledge distillation from
  larger verifiers, sLMs struggle with verification tasks requiring memorization,
  such as numerical calculations and fact-checking. To address this limitation,
  we propose Tool-integrated self-verification (T1), which delegates
  memorization-heavy verification steps to external tools, such as a code
  interpreter. Our theoretical analysis shows that tool integration reduces
  memorization demands and improves test-time scaling performance. Experiments on
  the MATH benchmark demonstrate that, with T1, a Llama-3.2 1B model under
  test-time scaling outperforms the significantly larger Llama-3.1 8B model.
  Moreover, T1 generalizes effectively to both mathematical (MATH500) and
  multi-domain knowledge-intensive tasks (MMLU-Pro). Our findings highlight the
  potential of tool integration to substantially improve the self-verification
  abilities of sLMs.
paper_ai_summary: Tool-integrated self-verification enhances small language models' performance in self-verification tasks and outperforms larger models on benchmarks.
paper_ai_keywords: test-time compute scaling small language models sLMs knowledge distillation memorization numerical calculations fact-checking Tool-integrated self-verification T1 MATH benchmark Llama-3.2 1B Llama-3.1 8B MATH500 MMLU-Pro self-verification tool integration