@inproceedings{yang-etal-2025-selfgoal,
    title = "{SELFGOAL}: Your Language Agents Already Know How to Achieve High-level Goals",
    author = "Yang, Ruihan  and
      Chen, Jiangjie  and
      Zhang, Yikai  and
      Yuan, Siyu  and
      Chen, Aili  and
      Richardson, Kyle  and
      Xiao, Yanghua  and
      Yang, Deqing",
    editor = "Chiruzzo, Luis  and
      Ritter, Alan  and
      Wang, Lu",
    booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.naacl-long.36/",
    pages = "799--819",
    ISBN = "979-8-89176-189-6",
    abstract = "Language agents powered by large language models (LLMs) are increasingly valuable as decision-making tools in domains such as gaming and programming. However, these agents often face challenges in achieving high-level goals without detailed instructions and in adapting to environments where feedback is delayed. In this paper, we present SELFGOAL, a novel automatic approach designed to enhance agents' capabilities to achieve high-level goals with limited human prior and environmental feedback. The core concept of SELFGOAL involves adaptively breaking down a high-level goal into a tree structure of more practical subgoals during the interaction with environments while identifying the most useful subgoals and progressively updating this structure. Experimental results demonstrate that SELFGOAL significantly enhances the performance of language agents across various tasks, including competitive, cooperative, and deferred feedback environments."
}