https://github.com/evolvinglmms-lab/mgpo

High-Resolution Visual Reasoning via Multi-Turn Grounding-Based Reinforcement Learning
https://github.com/evolvinglmms-lab/mgpo

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High-Resolution Visual Reasoning via Multi-Turn Grounding-Based Reinforcement Learning

• Host: GitHub
• URL: https://github.com/evolvinglmms-lab/mgpo
• Owner: EvolvingLMMs-Lab
• Created: 2025-05-27T05:12:49.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2025-07-23T03:59:26.000Z (12 months ago)
• Last Synced: 2025-12-25T04:06:39.200Z (6 months ago)
• Homepage: https://www.lmms-lab.com/posts/highres_visual_reasoning
• Size: 41.4 MB
• Stars: 52
• Watchers: 2
• Forks: 0
• Open Issues: 4
• Metadata Files:
  • Readme: README.md

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README

          
# High-Resolution Visual Reasoning via Multi-Turn Grounding-Based Reinforcement Learning

[![MGPO](https://img.shields.io/badge/Paper-MGPO-red)](https://arxiv.org/abs/2507.05920) [![MGPO](https://img.shields.io/badge/Blog-MGPO-blue)](https://www.lmms-lab.com/posts/highres_visual_reasoning) [![机器之心](https://img.shields.io/badge/公众号-机器之心-green)](https://mp.weixin.qq.com/s/K_MOiW2wgVGf5tkjSleyKQ) 

## 💡 Introduction

Inspired by the human visual system's top-down, task-driven search, we propose **Multi-turn Grounding-based Policy Optimization (MGPO)**. MGPO equips LMMs with interpretable, iterative visual grounding: the model predicts key regions, crops sub-images, and reasons over both the original and focused views.

**Key advantages:**

- **Interpretable, Top-down Visual Reasoning:** MGPO highlights which image regions are attended to at each step.

- **Breaks Pixel Limits:** Even if the full image is blurry due to resizing, MGPO identifies and crops clear sub-images for further analysis.

- **No Extra Grounding Annotations Needed:** MGPO is trained only with binary answer correctness, yet learns robust grounding.

## 🚀 Training Code

Our code is based on verl, training code and script are available at 

https://github.com/xinyu1205/verl/blob/mgpo/examples/grpo_trainer/run_qwen2_5_vl-7b_mgpo.sh

## 🧰 Experiments

### Visualizations



  





  (Examples of models trained with multi-turn grounding-based RL on high-resolution realworld tasks. The model first identifies key regions, which are then automatically cropped and returned as sub-images. Notably, despite only a binary reward function derived from the correctness of the final answer, the model gradually emerge robust grounding capability throughout the RL process.)



### Main Results

- **MGPO outperforms both SFT and GRPO** on high-resolution tasks.

- **+5.4%** on MME-Realworld (ID), **+5.2%** on V* Bench (OOD) over GRPO baseline.

- Surpasses OpenAI’s o1 and GPT-4o on V* Bench, despite using a smaller model and less data.



  



## ✒️ Citation

If you find our work to be useful for your research, please consider citing.

```bibtex

@misc{huang2025highresolutionvisualreasoningmultiturn,

      title={High-Resolution Visual Reasoning via Multi-Turn Grounding-Based Reinforcement Learning}, 

      author={Xinyu Huang and Yuhao Dong and Weiwei Tian and Bo Li and Rui Feng and Ziwei Liu},

      year={2025},

      eprint={2507.05920},

      archivePrefix={arXiv},

      primaryClass={cs.CV},

      url={https://arxiv.org/abs/2507.05920}, 

}

```