---
base_model:
- Qwen/Qwen2.5-VL-7B-Instruct
license: apache-2.0
library_name: transformers
pipeline_tag: video-text-to-text
---

# PyVision-Video-7B-SFT

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

This is **PyVision-Video-7B-SFT**, post-trained from [Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct).

- **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:** [arXiv:2602.20739](https://arxiv.org/abs/2602.20739)

## Model Description
PyVision-Video is part of the PyVision-RL framework, which aims to stabilize Reinforcement Learning (RL) training for open-weight multimodal models to sustain agentic interaction. 

For video reasoning, PyVision-Video employs an **on-demand context construction** strategy. It selectively samples task-relevant frames during the reasoning process, which significantly reduces visual token usage while maintaining strong performance on complex video understanding tasks. This model serves as the Supervised Fine-Tuning (SFT) checkpoint before RL training.

## 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}
}
```