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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.
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