@inproceedings{zhou-etal-2025-debate,
    title = "Debate, Reflect, and Distill: Multi-Agent Feedback with Tree-Structured Preference Optimization for Efficient Language Model Enhancement",
    author = "Zhou, Xiaofeng  and
      Huang, Heyan  and
      Liao, Lizi",
    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/corrections-2025-08/2025.findings-acl.475/",
    doi = "10.18653/v1/2025.findings-acl.475",
    pages = "9122--9137",
    ISBN = "979-8-89176-256-5",
    abstract = "Large Language Models (LLMs) continue to set new standards in knowledge-intensive and complex reasoning tasks, yet their high computational demands limit widespread adoption. While distilling large models into smaller ones offers a sustainable solution, current techniques{---}such as static knowledge distillation, resource-intensive reinforcement learning from human feedback, or limited self-reflection{---}struggle to yield substantial and lasting performance gains. In this paper, we present a novel Debate and Reflect (D{\&}R) framework that orchestrates multi-turn debates between smaller models and stronger teacher models, eliciting actionable feedback (e.g., error analysis, corrective strategies) to guide student models. Further, we introduce Tree-structured Direct Preference Optimization (T-DPO) to efficiently leverage these debate logs, organizing interactions into a hierarchical format for effective training. Empirical evaluations across diverse NLP benchmarks demonstrate that our approach significantly improves smaller-model accuracy, robustness, and generalization, outperforming conventional baselines by a large margin."
}