@inproceedings{wu-etal-2026-enhancing,
    title = "Enhancing Long-Chain Reasoning Distillation through Error-Aware Self-Reflection",
    author = "Wu, Zhuoyang  and
      Li, Xinze  and
      Liu, Zhenghao  and
      Yan, Yukun  and
      Liu, Zhiyuan  and
      Yu, Minghe  and
      Yang, Cheng  and
      Gu, Yu  and
      Yu, Ge  and
      Sun, Maosong",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.72/",
    pages = "1465--1482",
    ISBN = "979-8-89176-395-1",
    abstract = "Large Language Models (LLMs) have exhibited strong reasoning capabilities and achieved remarkable performance in mathematical problem-solving tasks. Recently, distilling reasoning ability from long-form Chains-of-Thought (CoTs) has emerged as a promising approach for enhancing Small Language Models (SLMs). Existing studies typically treat SLMs as student models and use long-form CoTs as supervision signals for Supervised Fine-Tuning (SFT) to transfer reasoning ability. However, such long-form CoT teachers are usually unaware of the student model{'}s capacity, which limits the effective utilization of the provided reasoning traces. To overcome this limitation, we propose error-aware self-reflection (ORION), a framework that refines teacher CoTs through an Error-Aware Reflection process. ORION enables the student model to construct more tailored teacher CoTs by refining teacher CoTs and incorporating its own reasoning errors. Experiments on multiple mathematical reasoning benchmarks demonstrate that ORION consistently improves performance by more than 2{\%} over all baselines. Further analysis reveals that the CoTs constructed by ORION exhibit higher coherence and logical consistency, thereby serving as more effective supervision signals for SFT. All codes are available at https://github.com/NEUIR/ORION."
}