ActionStudio: A Lightweight Framework for Data and Training of Action Models
🤖 AI Summary
Fine-tuning large action models for autonomous agent tasks is challenging due to environmental diversity and heterogeneous trajectory data. Method: This paper introduces the first standardized trajectory representation and end-to-end training framework specifically designed for action models. It unifies multi-source agent trajectory formats, supports efficient LoRA fine-tuning, full-parameter fine-tuning, and large-scale distributed training, and integrates automated data cleaning and consistency verification modules. Contribution/Results: Our framework bridges the critical gap in environment-specific fine-tuning infrastructure for agent systems. Extensive evaluation on both public and industrial-scale benchmarks demonstrates its efficiency and strong scalability. The codebase and datasets are publicly released, significantly lowering the barrier to customized action model training.
Application Category
📝 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 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.
Problem
Research questions and friction points this paper is trying to address.
Training large action models faces environmental diversity challenges
Existing infrastructure lacks scalable agent-specific fine-tuning support
Unifying heterogeneous agent trajectories requires standardized data formats
Innovation
Methods, ideas, or system contributions that make the work stand out.
Standardizes agent trajectories for unified processing
Supports LoRA, full fine-tuning, distributed training
Integrates preprocessing and verification tools
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