BEPA-7B-S2

<h1 style="display: flex; align-items: center; justify-content: center; gap: 10px;"> <img src="https://leon-gittech.github.io/Verl_GUI/icon.png" alt="BEPA" style="height: 0.9em; width: auto;"> <span>BEPA-7B-S2</span> </h1> <p align="center"> <b>From Off-Policy to On-Policy: Enhancing GUI Agents via Bi-level Expert-to-Policy Assimilation</b> </p> <p align="center"> <a href="https://leon-gittech.github.io/Verl_GUI/">🌐 Project Page</a> | <a href="https://arxiv.org/abs/2601.05787">📑 arXiv Paper</a> | <a href="https://github.com/LEON-gittech/Verl_GUI.git">💻 GitHub</a> </p> <p align="center"> 🏆 <b>#1 Open-Source End-to-End Model on OSWorld (15 steps)</b>: Achieves <b>32.13%</b> success rate<br> 📊 <b>Extreme Data Efficiency</b>: Matches GUI-OWL-7B performance using only <b>128 training tasks</b> </p>

Model Description

BEPA-7B-S2 is a GUI agent model fine-tuned from UI-TARS-1.5-7B using the BEPA (Bi-Level Expert-to-Policy Assimilation) framework. This model achieves state-of-the-art performance among open-source end-to-end models on the OSWorld benchmark.

Key Results

BEPA improves UI-TARS-1.5-7B from 22.87% to 32.13% on OSWorld-Verified (+9.26 points, +40.5% relative improvement).

BEPA Framework

<p align="center"> <img src="https://leon-gittech.github.io/Verl_GUI/stats/overview.png" alt="BEPA Overview" width="90%"> </p>

BEPA addresses two key challenges when using expert trajectories for training end-to-end GUI policies:

1. Structural Mismatch: Framework traces interleave multiple roles (planning, execution, grounding) that end-to-end policies cannot directly imitate.
2. Distribution Gap: Even after format conversion, trajectories remain far from the base-policy manifold.

LEVEL-1: Self-Rolled Execution

Transforms alien expert traces into policy-compatible trajectories by abstracting expert trajectories into compact natural-language plans, then letting the base policy act in the environment with plan conditioning.

LEVEL-2: Self-Aligned Assimilation

Dynamically maintains a per-task cache, injecting guided trajectories into GRPO updates only upon total on-policy failure. The cache is continuously refreshed with the policy's own successful executions.

Citation

@misc{wang2026offpolicyonpolicyenhancinggui,
      title={From Off-Policy to On-Policy: Enhancing GUI Agents via Bi-level Expert-to-Policy Assimilation},
      author={Zezhou Wang and Ziyun Zhang and Xiaoyi Zhang and Zhuzhong Qian and Yan Lu},
      year={2026},
      eprint={2601.05787},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2601.05787},
}

License

This model is released under the MIT License.

Acknowledgements

• veRL for the RL framework
• vLLM for fast inference
• OSWorld for the benchmark
• UI-TARS for the base model