---
license: apache-2.0
library_name: transformers
pipeline_tag: image-text-to-text
base_model: Qwen/Qwen2.5-VL-7B-Instruct
tags:
- multimodal
- agent
- reinforcement-learning
- qwen
---

# PyVision-Image-7B-RL

[PyVision-RL: Forging Open Agentic Vision Models via RL](https://arxiv.org/abs/2602.20739)

This is **PyVision-Image-7B-RL**, a multimodal agentic vision model post-trained from [Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct) using the PyVision-RL reinforcement learning framework.

- **Project Page:** [https://agent-x.space/pyvision-rl/](https://agent-x.space/pyvision-rl/)
- **Repository:** [https://github.com/agents-x-project/PyVision-RL](https://github.com/agents-x-project/PyVision-RL)
- **Paper:** [https://arxiv.org/abs/2602.20739](https://arxiv.org/abs/2602.20739)

## Description

Reinforcement learning for agentic multimodal models often suffers from "interaction collapse," where models learn to reduce tool usage and multi-turn reasoning. PyVision-RL is a framework designed to stabilize training and sustain interaction using an oversampling-filtering-ranking rollout strategy combined with an accumulative tool reward. 

PyVision-Image-7B-RL is specifically optimized for image understanding tasks and sustained multi-turn tool interaction, demonstrating strong performance and efficiency for scalable multimodal agents.

## Citation

If you find this work useful, please cite the following paper:

```bibtex
@article{pyvisionrl2026,
  title={PyVision-RL: Forging Open Agentic Vision Models via RL},
  author={Zhao, Shitian and Lin, Shaoheng and Li, Ming and Zhang, Haoquan and Peng, Wenshuo and Zhang, Kaipeng and Wei, Chen},
  journal={arXiv:2602.20739},
  year={2026}
}
```