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.

翻译：动作模型对于使自主代理能够执行复杂任务至关重要。然而，由于代理环境的多样性和代理数据的复杂性，训练大型动作模型仍然具有挑战性。尽管兴趣日益增长，但现有基础设施对可扩展的、特定于代理的微调支持有限。我们提出了ActionStudio，一个为大型动作模型设计的轻量级且可扩展的数据与训练框架。ActionStudio通过标准化格式统一异构的代理轨迹，支持包括LoRA、全参数微调和分布式设置在内的多种训练范式，并集成了稳健的预处理和验证工具。我们在公开和现实的行业基准上验证了其有效性，展示了强大的性能和实际的可扩展性。我们在 https://github.com/SalesforceAIResearch/xLAM 开源了代码和数据，以促进社区研究。