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English(EN) Trace2World: Reconstructing App Worlds from Real Interaction Traces for Scalable GUI Agent Training

Trace2World 重建应用世界以进行可扩展的GUI代理训练

研究人员开发了Trace2World,一个新颖的系统,旨在从真实用户交互轨迹重建应用程序世界,以进行更具可扩展性的GUI代理训练。该方法将全局行为重建与实现分离,创建了一个可编译为可执行环境的共享应用程序模型。与现有基线相比,Trace2World在重现工作流程和在真实设备上保持模拟训练收益方面表现出显著的改进。该系统还提高了代理在各种GUI基准测试和真实设备任务上的性能。 AI

影响 通过改进从真实世界数据中重建环境,实现更具可扩展性和准确性的GUI代理训练。

排序理由 该集群包含一篇详细介绍GUI代理训练新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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Trace2World 重建应用世界以进行可扩展的GUI代理训练

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Tool
该集群包含一篇详细介绍GUI代理训练新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]
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Topics
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完整方法见我们的编辑标准。

报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Zezhou Wang, Hanzhang Zhou, Panrong Tong, Quyu Kong, Pengxiang Li, Steven Hoi, Zhuzhong Qian, Yue Wang ·

    Trace2World: Reconstructing App Worlds from Real Interaction Traces for Scalable GUI Agent Training

    arXiv:2610.30792v1 Announce Type: new Abstract: Scaling GUI reinforcement learning requires environments that preserve real-app behavior. Yet coupling behavior inference with coding can turn partial traces into runnable mocks with incorrect state transitions. We introduce \textbf…