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Norsk(NO) OpAgent: Operator Agent for Web Navigation

OpAgent 在网页导航任务中成功率达到 71.6%

研究人员开发了 OpAgent,这是一种新颖的网页导航代理,它利用在线强化学习来克服静态数据集的局限性。该代理采用了一种分层多任务微调方法,结合了视觉语言模型和一个具有混合奖励机制的专业 RL 管道。OpAgent 在 WebArena 基准测试中表现出显著的性能提升,成功率达到 71.6%,超过了此前的最先进水平。 AI

影响 OpAgent 在 WebArena 上的 SOTA 性能可能会加速对更强大、更适应性强的网络代理的研究,以应对复杂的在线任务。

排序理由 这是一篇详细介绍新代理架构和基准性能的研究论文。

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

OpAgent 在网页导航任务中成功率达到 71.6%

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报道来源 [1]

  1. arXiv cs.AI TIER_1 Norsk(NO) · Yuyu Guo, Wenjie Yang, Siyuan Yang, Ziyang Liu, Cheng Chen, Yuan Wei, Yun Hu, Yang Huang, Guoliang Hao, Dongsheng Yuan, Jianming Wang, Xin Chen, Hang Yu, Lei Lei, Peng Di ·

    OpAgent: 用于网页导航的操作员 Agent

    arXiv:2602.13559v2 Announce Type: replace Abstract: To fulfill user instructions, autonomous web agents must contend with the inherent complexity and volatile nature of real-world websites. Conventional paradigms predominantly rely on Supervised Fine-Tuning (SFT) or Offline Reinf…