ActionStudio：一個輕量級框架，用於大型動作模型的數據處理與訓練

ActionStudio: A Lightweight Framework for Data and Training of Large Action Models

March 28, 2025

作者: Jianguo Zhang, Thai Hoang, Ming Zhu, Zuxin Liu, Shiyu Wang, Tulika Awalgaonkar, Akshara Prabhakar, Haolin Chen, Weiran Yao, Zhiwei Liu, Juntao Tan, Juan Carlos Niebles, Shelby Heinecke, Huan Wang, Silvio Savarese, Caiming Xiong

cs.AI

摘要

動作模型對於使自主代理能夠執行複雜任務至關重要。然而，由於代理環境的多樣性以及代理數據的複雜性，訓練大型動作模型仍然具有挑戰性。儘管興趣日益增長，現有基礎設施對可擴展、代理專屬的微調支持有限。我們提出了ActionStudio，這是一個專為大型動作模型設計的輕量級且可擴展的數據與訓練框架。ActionStudio通過標準化格式統一了異構的代理軌跡，支持包括LoRA、完整微調和分佈式設置在內的多樣化訓練範式，並整合了強大的預處理與驗證工具。我們在公開和現實的產業基準上驗證了其有效性，展示了強大的性能和實際的可擴展性。我們在https://github.com/SalesforceAIResearch/xLAM開源了代碼和數據，以促進社區的研究。

English

Action models are essential for enabling autonomous agents to perform complex tasks. However, training large action models remains challenging due to the diversity of agent environments and the complexity of agentic data. Despite growing interest, existing infrastructure provides limited support for scalable, agent-specific fine-tuning. We present ActionStudio, a lightweight and extensible data and training framework designed for large action models. ActionStudio unifies heterogeneous agent trajectories through a standardized format, supports diverse training paradigms including LoRA, full fine-tuning, and distributed setups, and integrates robust preprocessing and verification tools. We validate its effectiveness across both public and realistic industry benchmarks, demonstrating strong performance and practical scalability. We open-sourced code and data at https://github.com/SalesforceAIResearch/xLAM to facilitate research in the community.