@inproceedings{zhang-etal-2025-xlam,
title = "x{LAM}: A Family of Large Action Models to Empower {AI} Agent Systems",
author = "Zhang, Jianguo and
Lan, Tian and
Zhu, Ming and
Liu, Zuxin and
Hoang, Thai and
Kokane, Shirley and
Yao, Weiran and
Tan, Juntao and
Liu, Zhiwei and
Feng, Yihao and
Niebles, Juan Carlos and
Heinecke, Shelby and
Wang, Huan and
Savarese, Silvio and
Xiong, Caiming",
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://aclanthology.org/2025.naacl-long.578/",
doi = "10.18653/v1/2025.naacl-long.578",
pages = "11583--11597",
ISBN = "979-8-89176-189-6",
abstract = "Autonomous agents powered by large language models (LLMs) have attracted significant research interest. However, the open-source community faces many challenges in developing specialized models for agent tasks, driven by the scarcity of high-quality agent datasets and the absence of standard protocols in this area. We introduce xLAM, a series of large action models designed for AI agent tasks. The xLAM series includes five models with both dense and mixture-of-expert architectures, ranging from 1B to 8x22B parameters, trained using a scalable, flexible pipeline that unifies, augments, and synthesizes diverse datasets to enhance AI agents' generalizability and performance across varied environments. Our experimental results demonstrate that xLAM consistently delivers exceptional performance across multiple agent ability benchmarks, notably securing the 1st position on the Berkeley Function-Calling Leaderboard, outperforming GPT-4, Claude-3, and many other models in terms of tool use. By releasing the xLAM series, we aim to advance the performance of open-source LLMs for autonomous AI agents, potentially accelerating progress and democratizing access to high-performance models for agent tasks."
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<abstract>Autonomous agents powered by large language models (LLMs) have attracted significant research interest. However, the open-source community faces many challenges in developing specialized models for agent tasks, driven by the scarcity of high-quality agent datasets and the absence of standard protocols in this area. We introduce xLAM, a series of large action models designed for AI agent tasks. The xLAM series includes five models with both dense and mixture-of-expert architectures, ranging from 1B to 8x22B parameters, trained using a scalable, flexible pipeline that unifies, augments, and synthesizes diverse datasets to enhance AI agents’ generalizability and performance across varied environments. Our experimental results demonstrate that xLAM consistently delivers exceptional performance across multiple agent ability benchmarks, notably securing the 1st position on the Berkeley Function-Calling Leaderboard, outperforming GPT-4, Claude-3, and many other models in terms of tool use. By releasing the xLAM series, we aim to advance the performance of open-source LLMs for autonomous AI agents, potentially accelerating progress and democratizing access to high-performance models for agent tasks.</abstract>
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%0 Conference Proceedings
%T xLAM: A Family of Large Action Models to Empower AI Agent Systems
%A Zhang, Jianguo
%A Lan, Tian
%A Zhu, Ming
%A Liu, Zuxin
%A Hoang, Thai
%A Kokane, Shirley
%A Yao, Weiran
%A Tan, Juntao
%A Liu, Zhiwei
%A Feng, Yihao
%A Niebles, Juan Carlos
%A Heinecke, Shelby
%A Wang, Huan
%A Savarese, Silvio
%A Xiong, Caiming
%Y Chiruzzo, Luis
%Y Ritter, Alan
%Y Wang, Lu
%S 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)
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-189-6
%F zhang-etal-2025-xlam
%X Autonomous agents powered by large language models (LLMs) have attracted significant research interest. However, the open-source community faces many challenges in developing specialized models for agent tasks, driven by the scarcity of high-quality agent datasets and the absence of standard protocols in this area. We introduce xLAM, a series of large action models designed for AI agent tasks. The xLAM series includes five models with both dense and mixture-of-expert architectures, ranging from 1B to 8x22B parameters, trained using a scalable, flexible pipeline that unifies, augments, and synthesizes diverse datasets to enhance AI agents’ generalizability and performance across varied environments. Our experimental results demonstrate that xLAM consistently delivers exceptional performance across multiple agent ability benchmarks, notably securing the 1st position on the Berkeley Function-Calling Leaderboard, outperforming GPT-4, Claude-3, and many other models in terms of tool use. By releasing the xLAM series, we aim to advance the performance of open-source LLMs for autonomous AI agents, potentially accelerating progress and democratizing access to high-performance models for agent tasks.
%R 10.18653/v1/2025.naacl-long.578
%U https://aclanthology.org/2025.naacl-long.578/
%U https://doi.org/10.18653/v1/2025.naacl-long.578
%P 11583-11597
Markdown (Informal)
[xLAM: A Family of Large Action Models to Empower AI Agent Systems](https://aclanthology.org/2025.naacl-long.578/) (Zhang et al., NAACL 2025)
ACL
- Jianguo Zhang, Tian Lan, Ming Zhu, Zuxin Liu, Thai Hoang, Shirley Kokane, Weiran Yao, Juntao Tan, Zhiwei Liu, Yihao Feng, Juan Carlos Niebles, Shelby Heinecke, Huan Wang, Silvio Savarese, and Caiming Xiong. 2025. xLAM: A Family of Large Action Models to Empower AI Agent Systems. In 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), pages 11583–11597, Albuquerque, New Mexico. Association for Computational Linguistics.