@inproceedings{guo-etal-2026-failures,
    title = "Failures are Treasures: Constructing a Pedagogical Bridge for Agentic Strategy Distillation",
    author = "Guo, Jiaxin  and
      Sun, Hao  and
      Zhang, Wenhao  and
      Yang, Chunyu  and
      Zhang, Yan",
    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.938/",
    pages = "18808--18823",
    ISBN = "979-8-89176-395-1",
    abstract = {While Large Language Models (LLMs) excel in autonomous agent settings, small language models (SLMs) remain fragile, often collapsing after encountering errors. Traditional knowledge distillation focuses on imitating successful trajectories, while existing ``learning from mistakes'' methods treat errors as auxiliary signals rather than states requiring recoverable policies, leaving the dynamics of failure and recovery in agent settings largely unexplored. Inspired by Donald Sch{\"o}n{'}s theory of reflective practice, we propose P-BRIDGE (Pedagogical Bridge for Reflective Insight and Distillation of Guiding Errors). P-BRIDGE combines reflection-in-action with reflection-on-action, enabling agents to diagnose and correct critical errors during execution while abstracting transferable strategies from contrastive student{--}teacher trajectories. Experiments across eight benchmarks demonstrate that P-BRIDGE significantly elevates SLM performance{---}e.g., raising the 2WikiMultiHopQA accuracy of a 0.6B model from 6.2{\%} to 34.2{\%}.}
}