ActionStudio: A Lightweight Framework for Data and Training of Large
  Action Models
Authors: name: Jianguo Zhang; name: Thai Hoang; name: Ming Zhu; name: Zuxin Liu; name: Shiyu Wang; name: Tulika Awalgaonkar; name: Akshara Prabhakar; name: Haolin Chen; name: Weiran Yao; name: Zhiwei Liu; name: Juntao Tan; name: Juan Carlos Niebles; name: Shelby Heinecke; name: Huan Wang; name: Silvio Savarese; name: Caiming Xiong
Published: 2025-03-28 17:58:33

Abstract: 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.