Create README.md

README.md

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+---
+license: mit
+datasets:
+- PAPOGalaxy/PAPO_ViRL39K_train
+base_model:
+- Qwen/Qwen2.5-VL-7B-Instruct
+pipeline_tag: image-text-to-text
+library_name: transformers
+tags:
+- VGPO
+- Reinforcement learning
+- Multimodal Reasoning
+- Visual Attention Compensation
+---
+
+
+# Model Card for VGPO-RL-7B
+
+## 📖 Overview of VGPO
+
+  Standard RLVR methods treat every generated token equally, broadcasting a single reward signal indiscriminately. This leads to **signal dilution** — generic text tokens receive the same reinforcement as critical visually-grounded reasoning steps. Meanwhile, **temporal visual forgetting** causes attention to visual inputs to progressively decay as reasoning chains extend.
+  
+  VGPO addresses these issues through three key mechanisms:
+  - **Visual Attention Compensation (VAC):** Uses the inherent hidden-state similarity between generated tokens and image tokens as a *Visual Focus Score* to localize visual activations without external supervision. A progressive incentive schedule counteracts temporal visual forgetting in later reasoning steps.
+  - **Intra-Trajectory Re-weighting:** At the token level, dynamically re-weights advantages by visual focus scores to amplify learning from visually-grounded tokens.
+  - **Inter-Trajectory Re-weighting:** At the trajectory level, prioritizes rollouts with superior visual accumulation, favoring trajectories that sustain consistent visual grounding.
+
+## 🔗 Model Sources
+
+- **Github Repository:** [`VGPO`](https://github.com/wzb-bupt/VGPO)
+- **ArXiv Paper:** [`2604.09349`](https://arxiv.org/abs/2604.09349)
+
+
+## 📕 Training Datasets
+  
+  | Split | Dataset | Link |
+  |:------|:--------|:-----|
+  | Train | ViRL39K | [PAPOGalaxy/PAPO_ViRL39K_train](https://huggingface.co/datasets/PAPOGalaxy/PAPO_ViRL39K_train) |
+  | Val   | MMK12   | [PAPOGalaxy/PAPO_MMK12_test](https://huggingface.co/datasets/PAPOGalaxy/PAPO_MMK12_test) |
+
+
+## 📊 Evaluation
+  
+  We follow the evaluation script of [Look-Back](https://github.com/PKU-YuanGroup/Look-Back). All results are reported as **average accuracy** with inference temperature **0.0**. 
+
+  ### Supported Evaluation Benchmarks
+  
+  | Benchmark           | Focus Domain                               |
+  |:--------------------|:-------------------------------------------|
+  | MathVista           | General Mathematical & Geometric Reasoning |
+  | MathVerse           | General Mathematical & Geometric Reasoning |
+  | WeMath              | General Mathematical & Geometric Reasoning |
+  | MMK12               | General Mathematical & Geometric Reasoning |
+  | GeoMath             | General Mathematical & Geometric Reasoning |
+  | Geometry3K          | General Mathematical & Geometric Reasoning |
+  | LogicVista          | Vision-dependent Multimodal Reasoning      |
+  | SuperClevr Counting | Vision-dependent Multimodal Reasoning      |
+  | MMMU-Pro            | Vision-dependent Multimodal Reasoning      |
+  | MathVerse-V         | Vision-dependent Multimodal Reasoning      |
+  
+
+## ✍️ Citation
+
+  If you find this codebase useful in your research, please consider giving us a star ⭐ and citing our work 📝:
+  
+  ```bibtex
+  @article{wang2026vgpo,
+    title={Visually-Guided Policy Optimization for Multimodal Reasoning}, 
+    author={Zengbin Wang and Feng Xiong and Liang Lin and Xuecai Hu and Yong Wang and Yanlin Wang and Man Zhang and Xiangxiang Chu},
+    journal={arXiv preprint arXiv:2604.09349},
+    year={2026}
+  }
+```
+
+## ❤️ Acknowledgements
+
+  Our codebase is built upon [EasyR1](https://github.com/hiyouga/EasyR1), [VPPO-RL](https://github.com/huaixuheqing/VPPO-RL), [PAPO](https://github.com/MikeWangWZHL/PAPO), [Look-Back](https://github.com/PKU-YuanGroup/Look-Back). We thank the authors for their excellent work.
+
+