@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 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://aclanthology.org/2025.findings-acl.670/",
doi = "10.18653/v1/2025.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."
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<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.</abstract>
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%0 Conference Proceedings
%T LAM SIMULATOR: Advancing Data Generation for Large Action Model Training via Online Exploration and Trajectory Feedback
%A Hoang, Thai
%A Huang, Kung-Hsiang
%A Kokane, Shirley
%A Zhang, Jianguo
%A Liu, Zuxin
%A Zhu, Ming
%A Grigsby, Jake
%A Lan, Tian
%A Ryoo, Michael S.
%A Wu, Chien-Sheng
%A Heinecke, Shelby
%A Wang, Huan
%A Savarese, Silvio
%A Xiong, Caiming
%A Niebles, Juan Carlos
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F hoang-etal-2025-lam
%X 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.
%R 10.18653/v1/2025.findings-acl.670
%U https://aclanthology.org/2025.findings-acl.670/
%U https://doi.org/10.18653/v1/2025.findings-acl.670
%P 12921-12934
Markdown (Informal)
[LAM SIMULATOR: Advancing Data Generation for Large Action Model Training via Online Exploration and Trajectory Feedback](https://aclanthology.org/2025.findings-acl.670/) (Hoang et al., Findings 2025)
ACL
- Thai Hoang, Kung-Hsiang Huang, Shirley Kokane, Jianguo Zhang, Zuxin Liu, Ming Zhu, Jake Grigsby, Tian Lan, Michael S Ryoo, Chien-Sheng Wu, Shelby Heinecke, Huan Wang, Silvio Savarese, Caiming Xiong, and Juan Carlos Niebles. 2025. LAM SIMULATOR: Advancing Data Generation for Large Action Model Training via Online Exploration and Trajectory Feedback. In Findings of the Association for Computational Linguistics: ACL 2025, pages 12921–12934, Vienna, Austria. Association for Computational Linguistics.