@inproceedings{hu-etal-2025-astro,
    title = "{ASTRO}: Automatic Strategy Optimization For Non-Cooperative Dialogues",
    author = "Hu, Yikuan  and
      Huang, Chen  and
      Lei, Wenqiang",
    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/ingestion-acl-25/2025.findings-acl.22/",
    pages = "388--408",
    ISBN = "979-8-89176-256-5",
    abstract = "Non-cooperative dialogues, such as negotiations and persuasion, present significant challenges for large language models (LLMs) due to the lack of inherent cooperation or shared goals. Current methods for optimizing dialogue strategies require substantial human effort for strategy optimization. To address these challenges, we propose ASTRO (Automated Strategy Optimization), a fully automated solution that leverages LLMs' self-envolving capabilities. ASTRO dynamically generates customized strategy sets based on task goals and optimizes strategy planner using a self-play reinforcement learning paradigm. Our experimental results demonstrate ASTRO{'}s significant performance improvements over baseline models across various non-cooperative dialogue tasks, highlighting the potential for autonomously developing such agents without human intervention. Our code is available at https://github.com/SCUNLP/ASTRO."
}