| 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} | |
| } | |
| ``` |