@inproceedings{wei-etal-2025-webagent,
    title = "{W}eb{A}gent-R1: Training Web Agents via End-to-End Multi-Turn Reinforcement Learning",
    author = "Wei, Zhepei  and
      Yao, Wenlin  and
      Liu, Yao  and
      Zhang, Weizhi  and
      Lu, Qin  and
      Qiu, Liang  and
      Yu, Changlong  and
      Xu, Puyang  and
      Zhang, Chao  and
      Yin, Bing  and
      Yun, Hyokun  and
      Li, Lihong",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.401/",
    pages = "7920--7939",
    ISBN = "979-8-89176-332-6",
    abstract = "While reinforcement learning (RL) has demonstrated remarkable success in enhancing large language models (LLMs), it has primarily focused on single-turn tasks such as solving math problems. Training effective web agents for multi-turn interactions remains challenging due to the complexity of long-horizon decision-making across dynamic web interfaces. In this work, we present WebAgent-R1, a simple yet effective end-to-end multi-turn RL framework for training web agents. It learns directly from online interactions with web environments by asynchronously generating diverse trajectories, entirely guided by binary rewards depending on task success. Experiments on the WebArena-Lite benchmark demonstrate the effectiveness of WebAgent-R1, boosting the task success rate of Qwen-2.5-3B from 6.1{\%} to 33.9{\%} and LLaMA-3.1-8B from 8.5{\%} to 44.8{\%}, significantly outperforming existing state-of-the-art methods and strong proprietary models such as OpenAI o3. In-depth analyses reveal the effectiveness of the thinking-based prompting strategy and test-time scaling through increased interactions for web tasks. We further investigate different RL initialization policies by introducing two variants, namely WebAgent-R1-Zero and WebAgent-R1-CoT, which highlight the importance of the warm-up training stage (i.e., behavior cloning) and provide insights on incorporating long chain-of-thought (CoT) reasoning in web agents."
}