WebAgent-R1: Training Web Agents via End-to-End Multi-Turn Reinforcement
  Learning
Authors: name: Zhepei Wei, name: Wenlin Yao, name: Yao Liu, name: Weizhi Zhang, name: Qin Lu, name: Liang Qiu, name: Changlong Yu, name: Puyang Xu, name: Chao Zhang, name: Bing Yin, name: Hyokun Yun, name: Lihong Li

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.