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Training Needs Trustworthy Worlds: Verified Synthetic Web Environments for Agent Learning

Zhang, Chenghao et al.
arXiv (All) · Papers
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Web agents promise to automate complex digital workflows, but their training remains limited by synthetic environments that look plausible while hiding broken links, inconsistent states, or infeasible tasks. We address the gap between scalable environment generation and trustworthy agent learning by
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