PyVision-Video-7B-SFT

PyVision-Video-7B-SFT

PyVision-RL: Forging Open Agentic Vision Models via RL

This is PyVision-Video-7B-SFT, post-trained from Qwen2.5-VL-7B-Instruct.

• Project Page: https://agent-x.space/pyvision-rl/
• Repository: https://github.com/agents-x-project/PyVision-RL
• Paper: arXiv: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:

@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}
}