OpenWebRL: Demystifying Online Multi-turn Reinforcement Learning for Visual Web Agents 事件

OpenWebRL: Demystifying Online Multi-turn Reinforcement Learning for Visual Web Agents arXiv:2606.02031v1 Announce Type: cross Abstract: Building capable visual web agents requires long-horizon reasoning, precise grounding, and robust interaction with dynamic real-world websites. Despite rapid progress, the strongest systems remain largely proprietary, while open agents still depend heavily on supervised post-training over large collections of curated web trajectories. This dependence creates a