@inproceedings{chen-etal-2025-unveiling-key,
    title = "Unveiling the Key Factors for Distilling Chain-of-Thought Reasoning",
    author = "Chen, Xinghao  and
      Sun, Zhijing  and
      Wenjin, Guo  and
      Zhang, Miaoran  and
      Chen, Yanjun  and
      Sun, Yirong  and
      Su, Hui  and
      Pan, Yijie  and
      Klakow, Dietrich  and
      Li, Wenjie  and
      Shen, Xiaoyu",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/display_plenaries/2025.findings-acl.782/",
    pages = "15094--15119",
    ISBN = "979-8-89176-256-5",
    abstract = "Large Language Models (LLMs) excel in reasoning tasks through Chain-of-Thought (CoT) prompting. However, CoT prompting greatly increases computational demands, which has prompted growing interest in distilling CoT capabilities into Small Language Models (SLMs). This study systematically examines the factors influencing CoT distillation, including the choice of granularity, format and teacher model. Through experiments involving four teacher models and seven student models across seven mathematical and commonsense reasoning datasets, we uncover three key findings: (1) Unlike LLMs, SLMs exhibit a *non-monotonic* relationship with granularity, with stronger models benefiting from finer-grained reasoning and weaker models performing better with simpler CoT supervision; (2) CoT format significantly impacts LLMs but has *minimal* effect on SLMs, likely due to their reliance on supervised fine-tuning rather than pretraining preferences; (3) Stronger teacher models do *NOT* always produce better student models, as diversity and complexity in CoT supervision can outweigh accuracy alone. These findings emphasize the need to tailor CoT strategies to specific student model, offering actionable insights for optimizing CoT distillation in SLMs."
}