ActionStudio：一个轻量级框架，用于大规模动作模型的数据处理与训练

摘要

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，训练大规模动作模型仍然具有挑战性。尽管关注度日益增加，现有基础设施对可扩展的、针对代理的微调支持有限。我们推出了ActionStudio，一个轻量级且可扩展的数据和训练框架，专为大规模动作模型设计。ActionStudio通过标准化格式统一了异构的代理轨迹，支持包括LoRA、全量微调和分布式设置在内的多种训练范式，并集成了强大的预处理和验证工具。我们在公开和实际行业基准上验证了其有效性，展示了卓越的性能和实际可扩展性。我们在https://github.com/SalesforceAIResearch/xLAM开源了代码和数据，以促进社区研究。

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.

ActionStudio：一个轻量级框架，用于大规模动作模型的数据处理与训练

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

摘要

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