Agent World Model in the Wild: Computer-Use Agent Environment Synthesis· 野外环境中的智能体世界模型
Agent learning is shifting from learning from human data toward learning through interaction with the world. However, scaling such learning directly in real-world computer systems is challenging and can introduce substantial security risks. We therefore propose Agent World Model in the Wild (WildAWM), a scalable pipeline for synthesizing controllable environments and tasks for training general computer-use agents (CUAs). Whereas existing approaches typically specialize in a single agent interaction interface, we construct executable environments that jointly support graphical user interfaces, model context protocol services, and command-line interfaces, allowing agents to flexibly combine visual interaction, tool use, and code execution. WildAWM is entirely powered by open-source models, d
智能体在野外环境中通过合成可控环境学习使用计算机。
- 核心方法
- 提出WildAWM，使用开源模型合成支持GUI、模型服务和CLI的环境，使CUA能组合视觉、工具使用和代码执行进行交互学习。
- 适合谁读
- 研究者 / 工程师
- 要解决的问题
- 直接在现实世界计算机系统中扩展智能体学习面临安全和可扩展性挑战。
- 关键实验
- 未提供
- 主要贡献
- 提供了一种可扩展和安全的方法来训练通用计算机使用智能体，支持多种交互接口。
- 意义与局限
- 意义在于推动AI智能体向更真实、复杂的计算机使用环境迁移，潜在提升其泛化能力，但需进一步实验验证安全性和效果。