WebHarbor：为 GUI 智能体构建 153 个可执行真实网站环境
WebHarbor: Docking Real Websites for Evolving GUI Agent Environments
WebHarbor 提出一套 153 个可执行、Docker 化的网站环境，重建真实网页服务的界面、内容与功能流程，并保持确定性控制。该套件包含 3518 个任务，覆盖多步骤和登录后工作流，同时可用于成本更低的强化学习训练；在 WebHarbor 中用 RL 训练的 Qwen 4B 级 GUI 智能体可泛化到未见任务，在四个基准上与领先开源 GUI 网页智能体表现相当。代码与数据已公开。
Authors:Zhaoyang Wang, Qianhui Wu, Boyu Gou, Demi Wang, Jiacheng Zhang, Peichun Hua, Qize Li, Shi Qiu, Yifan Jiang, Yifan Zhang, Zexu Jin, Baolin Peng, Bolin Shen, Chetan Bansal, Dehan Kong, Deren Lei, Dongshen Peng, Fengqing Jiang, Hao Cheng, Haoqing Wang, Jiaming Shan, Jicheng Wang, Jingguang Zhou, Jixuan He, Kaide Zeng, Kangyu Zhu, Lama Moukheiber, Mufan Qiu, Peng Xia, Qihan Sun, Qingwei Lin, Qiushi Sun, Ruichen Zhang, Shuqing Luo, Si Qin, Sicheng Fan, Tao Xiong, Wenlin Yao, Xinyu Zhang, Xinyue Zeng, Xuanrui Li, Xuchao Zhang, Yian Wang, Yichao Liu, Yiming Liang, Yiyang Zhou, Yizhu Jiao, Yuetai Li, Yurun Chen, Yuxuan Zhang, Zheyuan Yang, Zhiyuan Yao, Zhongyang Li, Zihao Li, Zili Wang, Jianfeng Gao, Huaxiu Yao
Abstract:GUI web agents require realistic and interactive environments that remain stable during both evaluation and training. Live website evaluation suffers from changing content, access restrictions, and limited coverage of authenticated workflows, while existing synthetic environments for training often lack functional depth and visual fidelity. We propose WebHarbor, a suite of 153 executable and dockerized website environments that faithfully reconstruct the visual interfaces, content, and functional workflows of real-world web services under deterministic control. We build WebHarbor through task-driven human-agent collaboration, where environments evolve as new tasks expand their functional scope, coding agents implement the required changes, and human contributors audit visual fidelity, functional correctness, and task validity. The resulting suite comprises 3,518 tasks spanning multi-step and post-authentication workflows, where existing works often fail to cover. Beyond reliable evaluation, WebHarbor provides high-fidelity synthetic web environments for cost-efficient reinforcement learning (RL). We demonstrate that a Qwen 4B-level GUI agent trained with RL in WebHarbor generalizes to unseen tasks and performs competitively with leading open-source GUI web agents across four benchmarks. The code and data are publicly available at this https URL.
| Comments: | 31 pages |
| Subjects: | Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG) |
| Cite as: | arXiv:2611.03634 [cs.AI] |
| (or arXiv:2611.03634v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2611.03634 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Zhaoyang Wang [view email]
[v1]
Mon, 2 Nov 2026 18:57:36 UTC (10,987 KB)
来源：arXiv cs.CL · arxiv.org