T1: Tool-integrated Self-verification for Test-time Compute Scaling in
  Small Language Models
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.
67f48eff463c4d1c4ae5284b
Minki Kang
admin_assigned
2025-04-08T09:12:42.979Z
64b74920fe6a108d03fed767
/avatars/a2c05b809c36fa5fab8e1a43b3e67051.svg
Minki Kang
user
Nardien
67f48eff463c4d1c4ae5284c
Jongwon Jeong
67f48eff463c4d1c4ae5284d
Jaewoong Cho
T1: Tool-integrated Self-verification for Test-time Compute Scaling in
  Small Language Models
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.
Tool-integrated self-verification enhances small language models' performance in self-verification tasks and outperforms larger models on benchmarks.
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