Papers with web tasks
WebAgent-R1: Training Web Agents via End-to-End Multi-Turn Reinforcement Learning (2025.emnlp-main)
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Zhepei Wei, Wenlin Yao, Yao Liu, Weizhi Zhang, Qin Lu, Liang Qiu, Changlong Yu, Puyang Xu, Chao Zhang, Bing Yin, Hyokun Yun, Lihong Li
| Challenge: | Existing work on reinforcement learning has focused on single-turn tasks such as solving math problems. |
| Approach: | They propose a framework that learns directly from online interactions by asynchronously generating diverse trajectories, guided by binary rewards depending on task success. |
| Outcome: | Experiments on the WebArena-Lite benchmark show that the framework outperforms state-of-the-art methods and strong proprietary models. |
MMInA: Benchmarking Multihop Multimodal Internet Agents (2025.findings-acl)
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| Challenge: | Existing benchmarks fail to assess embodied agents in a realistic, evolving environment for compositional Internet tasks. |
| Approach: | They propose a multihop and multimodal benchmark to evaluate embodied agents for compositional Internet tasks. |
| Outcome: | The proposed protocol significantly improves the performance of both the single-hop and multihop web browsing abilities. |