title: ActionStudio: A Lightweight Framework for Data and Training of Large
    Action Models
summary: 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.
paper_authors._id: 67ec2f2882007f02949fdbed
paper_authors.name: Jianguo Zhang
paper_authors._id: 67ec2f2882007f02949fdbee
paper_authors.name: Thai Hoang
paper_authors._id: 67ec2f2882007f02949fdbef
paper_authors.name: Ming Zhu
paper_authors._id: 67ec2f2882007f02949fdbf0
paper_authors.name: Zuxin Liu
paper_authors._id: 67ec2f2882007f02949fdbf1
paper_authors.name: Shiyu Wang
paper_authors._id: 67ec2f2882007f02949fdbf2
paper_authors.name: Tulika Awalgaonkar
paper_authors._id: 67ec2f2882007f02949fdbf3
paper_authors.name: Akshara Prabhakar
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-04T07:09:51.777Z
paper_authors._id: 6475718de0b188d3cb25f346
paper_authors.avatarUrl: /avatars/b0cdca7e19bbc1dbac00ea664108b51e.svg
paper_authors.fullname: Akshara Prabhakar
paper_authors.type: user
paper_authors.user: aksh555
paper_authors._id: 67ec2f2882007f02949fdbf4
paper_authors.name: Haolin Chen
paper_authors._id: 67ec2f2882007f02949fdbf5
paper_authors.name: Weiran Yao
paper_authors._id: 67ec2f2882007f02949fdbf6
paper_authors.name: Zhiwei Liu
paper_authors._id: 67ec2f2882007f02949fdbf7
paper_authors.name: Juntao Tan
paper_authors._id: 67ec2f2882007f02949fdbf8
paper_authors.name: Juan Carlos Niebles
paper_authors._id: 67ec2f2882007f02949fdbf9
paper_authors.name: Shelby Heinecke
paper_authors._id: 67ec2f2882007f02949fdbfa
paper_authors.name: Huan Wang
paper_authors._id: 67ec2f2882007f02949fdbfb
paper_authors.name: Silvio Savarese
paper_authors._id: 67ec2f2882007f02949fdbfc
paper_authors.name: Caiming Xiong
paper_title: ActionStudio: A Lightweight Framework for Data and Training of Large
    Action Models
paper_summary: 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.
paper_ai_summary: ActionStudio is a lightweight, extensible framework for training large action models that supports scalability, diverse training paradigms, and integrates preprocessing and verification tools, demonstrating strong performance on public and industry benchmarks.
paper_ai_keywords: ActionStudio agent trajectories standardized format LoRA full fine-tuning distributed setups preprocessing verification tools