Model

We release the RFT (Reward Fine-Tuned) model used in BrowserAgent, initialized from the SFT checkpoint of Qwen/Qwen2.5-7B-Instruct.
This model further optimizes browsing trajectories with task-level reward signals that encourage higher success rate, shorter action paths, and safer interactions.

Paper

BrowserAgent: Building Web Agents with Human-Inspired Web Browsing Actions

Project Page

https://tiger-ai-lab.github.io/BrowserAgent/

Code

https://github.com/TIGER-AI-Lab/BrowserAgent

Sample Usage

hf download TIGER-Lab/BrowserAgent-RFT --local-dir ./models/browseragent-rft --repo model

Citation

@misc{yu2025browseragentbuildingwebagents,
      title={BrowserAgent: Building Web Agents with Human-Inspired Web Browsing Actions}, 
      author={Tao Yu and Zhengbo Zhang and Zhiheng Lyu and Junhao Gong and Hongzhu Yi and Xinming Wang and Yuxuan Zhou and Jiabing Yang and Ping Nie and Yan Huang and Wenhu Chen},
      year={2025},
      eprint={2510.10666},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2510.10666}, 
}