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Revisiting the Capacity Gap in Chain-of-Thought Distillation from a Practical Perspective

Machine Learning 2026-04-13 v1 Artificial Intelligence Computation and Language

Abstract

Chain-of-thought (CoT) distillation transfers reasoning behaviors from a strong teacher to a smaller student, but prior work reports a capacity gap: distillation may fail when the teacher-student capability mismatch is large. We revisit the capacity gap from a practical perspective by re-examining commonly used experimental settings. Notably, we find that CoT distillation often degrades performance compared to the student's pre-distillation baseline, an issue obscured when only post-distillation comparisons are reported. We therefore propose a more realistic evaluation protocol and find that the impact of capacity gap effects does not consistently dominate across tasks and settings, especially when candidate teachers differ substantially in performance. Our results offer practical guidance for selecting teacher-student pairs in CoT distillation.

Keywords

Cite

@article{arxiv.2604.08880,
  title  = {Revisiting the Capacity Gap in Chain-of-Thought Distillation from a Practical Perspective},
  author = {Tokio Kajitsuka and Ukyo Honda and Sho Takase},
  journal= {arXiv preprint arXiv:2604.08880},
  year   = {2026}
}

Comments

19 pages, 6 figures