@inproceedings{hoang-etal-2025-lam,
    title = "{LAM} {SIMULATOR}: Advancing Data Generation for Large Action Model Training via Online Exploration and Trajectory Feedback",
    author = "Hoang, Thai Quoc  and
      Huang, Kung-Hsiang  and
      Kokane, Shirley  and
      Zhang, Jianguo  and
      Liu, Zuxin  and
      Zhu, Ming  and
      Grigsby, Jake  and
      Lan, Tian  and
      Ryoo, Michael S  and
      Wu, Chien-Sheng  and
      Heinecke, Shelby  and
      Wang, Huan  and
      Savarese, Silvio  and
      Xiong, Caiming  and
      Niebles, Juan Carlos",
    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/acl25-workshop-ingestion/2025.findings-acl.670/",
    pages = "12921--12934",
    ISBN = "979-8-89176-256-5",
    abstract = "Large Action Models (LAMs) for AI Agents offer incredible potential but face challenges due to the need for high-quality training data, especially for multi-steps tasks that involve planning, executing tool calls, and responding to feedback. To address these issues, we present LAM SIMULATOR, a comprehensive framework designed for online exploration of agentic tasks with high-quality feedback. Our framework features a dynamic task query generator, an extensive collection of tools, and an interactive environment where Large Language Model (LLM) Agents can call tools and receive real-time feedback. This setup enables LLM Agents to explore and solve tasks autonomously, facilitating the discovery of multiple approaches to tackle any given task. The resulting action trajectory data are then used to create high-quality training datasets for LAMs. Our experiments on popular agentic benchmarks, ToolBench and CRMArena, highlight the effectiveness of LAM SIMULATOR: models trained with self-generated datasets using our framework achieve significant performance gains, up to a 49.3{\%} improvement over their original baselines. LAM SIMULATOR requires minimal human input during dataset creation, highlighting LAM SIMULATOR{'}s efficiency and effectiveness in speeding up development of AI agents."
}