---
license: apache-2.0
library_name: transformers
pipeline_tag: video-text-to-text
tags:
- multimodal
- agent
- reinforcement-learning
---

# PyVision-Video-7B-RL

[**PyVision-RL: Forging Open Agentic Vision Models via RL**](https://huggingface.co/papers/2602.20739)

PyVision-Video-7B-RL is an open-weight agentic multimodal model post-trained from [Qwen2.5-VL-7B](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct) using reinforcement learning.

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

## Overview

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 by combining an oversampling-filtering-ranking rollout strategy with an accumulative tool reward.

**PyVision-Video** specifically addresses the challenge of video reasoning using **on-demand context construction**. It selectively samples task-relevant frames during the reasoning process to significantly reduce visual token usage while maintaining high performance on complex multimodal agentic tasks.

## Citation

```bibtex
@article{zhao2026pyvisionrl,
  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 preprint arxiv:2602.20739},
  year={2026},
}
```