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README.md

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+---
+license: mit
+library_name: transformers
+pipeline_tag: image-text-to-text
+tags:
+- gui-agent
+- rlvr
+- computer-use
+---
+
+# BEPA-7B-S2
+
+This repository contains the weights for **BEPA-7B-S2**, an end-to-end screenshot-to-action policy for GUI agents. The model was introduced in the paper [From Off-Policy to On-Policy: Enhancing GUI Agents via Bi-level Expert-to-Policy Assimilation](https://huggingface.co/papers/2601.05787).
+
+## Introduction
+
+**BEPA** (Bi-Level Expert-to-Policy Assimilation) is a framework designed to enhance Vision-Language Models acting as computer-use agents (CUAs). It addresses the challenges of using static expert trajectories in reinforcement learning from verifiable rewards (RLVR) by turning them into policy-aligned guidance.
+
+BEPA operates in two complementary stages:
+- **LEVEL-1 (Self-Rolled Execution):** Transforms alien expert traces into policy-compatible trajectories by abstracting them into natural-language plans and letting the base policy execute them.
+- **LEVEL-2 (Self-Aligned Assimilation):** Dynamically maintains a per-task cache that injects guided trajectories into training updates when on-policy failures occur.
+
+On the OSWorld-Verified benchmark, BEPA improves the success rate of UITARS1.5-7B from 22.87% to **32.13%**, establishing it as a top-performing open-source end-to-end model.
+
+## Resources
+- **Paper:** [https://huggingface.co/papers/2601.05787](https://huggingface.co/papers/2601.05787)
+- **Project Page:** [https://leon-gittech.github.io/Verl_GUI/](https://leon-gittech.github.io/Verl_GUI/)
+- **GitHub Repository:** [https://github.com/LEON-gittech/Verl_GUI](https://github.com/LEON-gittech/Verl_GUI)
+
+## Main Results
+
+| Method | Overall Success (%) |
+|--------|-------------|
+| UITARS1.5-7B | 22.87 |
+| GRPO | 23.60 |
+| **BEPA (ours)** | **32.13** |
+
+## Citation
+
+```bibtex
+@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}, 
+}
+```