@inproceedings{zhang-etal-2025-actionstudio,
title = "{A}ction{S}tudio: A Lightweight Framework for Data and Training of Large Action Models",
author = "Zhang, Jianguo and
Hoang, Thai Quoc and
Zhu, Ming and
Liu, Zuxin and
Wang, Shiyu and
Awalgaonkar, Tulika Manoj and
Prabhakar, Akshara and
Chen, Haolin and
Yao, Weiran and
Liu, Zhiwei and
Tan, Juntao and
Niebles, Juan Carlos and
Heinecke, Shelby and
Wang, Huan and
Savarese, Silvio and
Xiong, Caiming",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-main.1090/",
pages = "21499--21513",
ISBN = "979-8-89176-332-6",
abstract = "Large Action models are essential for enabling autonomous agents to perform complex tasks. However, training such models remains challenging due to the diversity of agent environments and the complexity of noisy agentic data. Existing infrastructure offers limited support for scalable, agent-specific fine-tuning and standardized agent data processing. We introduce \textbf{ActionStudio}, a lightweight and extensible data and training framework designed for large action models. ActionStudio unifies diverse agent trajectories using our proposed Unified Format 2.0, supports a range of training workflows with optimized multi-node distributed setup, and integrates robust preprocessing and real-time verification tools. ActionStudio demonstrates up to 9$\times$ higher throughput compared to existing agentic training frameworks, and our trained models yield top performances across public and realistic agent benchmarks. To support the broader research community, we open-source the ActionStudio framework and release \textit{actionstudio-98k}, a curated dataset of 98k high-quality trajectories."
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<abstract>Large Action models are essential for enabling autonomous agents to perform complex tasks. However, training such models remains challenging due to the diversity of agent environments and the complexity of noisy agentic data. Existing infrastructure offers limited support for scalable, agent-specific fine-tuning and standardized agent data processing. We introduce ActionStudio, a lightweight and extensible data and training framework designed for large action models. ActionStudio unifies diverse agent trajectories using our proposed Unified Format 2.0, supports a range of training workflows with optimized multi-node distributed setup, and integrates robust preprocessing and real-time verification tools. ActionStudio demonstrates up to 9\times higher throughput compared to existing agentic training frameworks, and our trained models yield top performances across public and realistic agent benchmarks. To support the broader research community, we open-source the ActionStudio framework and release actionstudio-98k, a curated dataset of 98k high-quality trajectories.</abstract>
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%0 Conference Proceedings
%T ActionStudio: A Lightweight Framework for Data and Training of Large Action Models
%A Zhang, Jianguo
%A Hoang, Thai Quoc
%A Zhu, Ming
%A Liu, Zuxin
%A Wang, Shiyu
%A Awalgaonkar, Tulika Manoj
%A Prabhakar, Akshara
%A Chen, Haolin
%A Yao, Weiran
%A Liu, Zhiwei
%A Tan, Juntao
%A Niebles, Juan Carlos
%A Heinecke, Shelby
%A Wang, Huan
%A Savarese, Silvio
%A Xiong, Caiming
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F zhang-etal-2025-actionstudio
%X Large Action models are essential for enabling autonomous agents to perform complex tasks. However, training such models remains challenging due to the diversity of agent environments and the complexity of noisy agentic data. Existing infrastructure offers limited support for scalable, agent-specific fine-tuning and standardized agent data processing. We introduce ActionStudio, a lightweight and extensible data and training framework designed for large action models. ActionStudio unifies diverse agent trajectories using our proposed Unified Format 2.0, supports a range of training workflows with optimized multi-node distributed setup, and integrates robust preprocessing and real-time verification tools. ActionStudio demonstrates up to 9\times higher throughput compared to existing agentic training frameworks, and our trained models yield top performances across public and realistic agent benchmarks. To support the broader research community, we open-source the ActionStudio framework and release actionstudio-98k, a curated dataset of 98k high-quality trajectories.
%U https://aclanthology.org/2025.emnlp-main.1090/
%P 21499-21513
Markdown (Informal)
[ActionStudio: A Lightweight Framework for Data and Training of Large Action Models](https://aclanthology.org/2025.emnlp-main.1090/) (Zhang et al., EMNLP 2025)
ACL
- Jianguo Zhang, Thai Quoc Hoang, Ming Zhu, Zuxin Liu, Shiyu Wang, Tulika Manoj Awalgaonkar, Akshara Prabhakar, Haolin Chen, Weiran Yao, Zhiwei Liu, Juntao Tan, Juan Carlos Niebles, Shelby Heinecke, Huan Wang, Silvio Savarese, and Caiming Xiong. 2025. ActionStudio: A Lightweight Framework for Data and Training of Large Action Models. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 21499–21513, Suzhou, China. Association for Computational Linguistics.