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  ---
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- license: apache-2.0
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  base_model:
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  - Qwen/Qwen2.5-VL-7B-Instruct
 
 
 
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  ---
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  [PyVision-RL: Forging Open Agentic Vision Models via RL](https://arxiv.org/abs/2602.20739)
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- This is PyVision-Video-7B-SFT, post trained from Qwen2.5-VL-7B.
 
 
 
 
 
 
 
 
 
 
 
 
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  ```bibtex
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  @article{pyvisionrl2026,
 
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  ---
 
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  base_model:
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  - Qwen/Qwen2.5-VL-7B-Instruct
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+ license: apache-2.0
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+ library_name: transformers
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+ pipeline_tag: video-text-to-text
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  ---
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+ # PyVision-Video-7B-SFT
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  [PyVision-RL: Forging Open Agentic Vision Models via RL](https://arxiv.org/abs/2602.20739)
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+ This is **PyVision-Video-7B-SFT**, post-trained from [Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct).
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+ - **Project Page:** [https://agent-x.space/pyvision-rl/](https://agent-x.space/pyvision-rl/)
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+ - **Repository:** [https://github.com/agents-x-project/PyVision-RL](https://github.com/agents-x-project/PyVision-RL)
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+ - **Paper:** [arXiv:2602.20739](https://arxiv.org/abs/2602.20739)
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+ ## Model Description
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+ 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.
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+ 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.
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+ ## Citation
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+ If you find this work useful, please cite the following paper:
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  ```bibtex
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  @article{pyvisionrl2026,