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--- |
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license: apache-2.0 |
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datasets: |
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- TIGER-Lab/BrowserAgent-Data |
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language: |
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- en |
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base_model: |
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- Qwen/Qwen2.5-7B-Instruct |
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metrics: |
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- success_rate |
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- trajectory_f1 |
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tags: |
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- agent |
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- browser |
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- web |
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- sft |
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--- |
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## Model |
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We release the **SFT (Supervised Fine-Tuned)** model used in **BrowserAgent**, based on `Qwen/Qwen2.5-7B-Instruct`. |
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This model learns structured web-browsing behaviors—such as **click, type, scroll, read, submit**—from human-style demonstrations and produces **schema-constrained action sequences** for browser environments. |
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- **Base:** Qwen2.5-7B-Instruct |
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- **Objective:** Next-token prediction on normalized, schema-validated browsing trajectories |
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- **Format:** JSON-like structured actions (compatible with BrowserAgent runtime) |
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## Data |
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The SFT data includes: |
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- Human and assisted browsing demonstrations |
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- Canonicalization under a unified action schema |
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- Filtering and de-duplication to ensure validity and safety |
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## Code |
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<https://github.com/TIGER-AI-Lab/BrowserAgent> |
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## Sample Usage |
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```bash |
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hf download TIGER-Lab/BrowserAgent-SFT --local-dir ./models/browseragent-sft --repo model |
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``` |
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## Citation |
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``` |
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@misc{yu2025browseragentbuildingwebagents, |
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title={BrowserAgent: Building Web Agents with Human-Inspired Web Browsing Actions}, |
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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}, |
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year={2025}, |
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eprint={2510.10666}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL}, |
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url={https://arxiv.org/abs/2510.10666}, |
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} |
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``` |
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