Create README.md

README.md

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+---
+base_model:
+- Qwen/Qwen2.5-VL-7B-Instruct
+language:
+- en
+license: apache-2.0
+pipeline_tag: image-text-to-text
+tags:
+- transformers
+- multimodal
+library_name: transformers
+---
+
+## Model Overview
+
+We present **Visual Game Learning (ViGaL)**, a novel post-training paradigm where multimodal large language models (MLLMs) develop out-of-domain generalization of multimodal reasoning through playing arcade-like games. 
+
+**ViGaL-7B** demonstrates that training a 7B-parameter MLLM via reinforcement learning on simple arcade-like games like Snake significantly enhances its downstream performance on multimodal math benchmarks like MathVista, and on multi-discipline questions like MMMU, **without seeing any worked solutions, equations, or diagrams during RL**, suggesting the capture of transferable reasoning skills.
+
+## Resources
+
+For details of our approach and performance comparison, please see our [paper](https://arxiv.org/abs/2506.08011).
+
+For details of training and evaluation, please see our [code repo](https://github.com/yunfeixie233/ViGaL).
+
+| [**🚀 Project Page**](https://yunfeixie233.github.io/ViGaL/) | [**📖 Paper**](https://arxiv.org/abs/2506.08011) | [**🔗 GitHub**](https://github.com/yunfeixie233/ViGaL) | **🤗 Training Data** (Coming Soon) |
+
+
+## Citation
+
+If you feel this model useful, please give us a free cite:
+```bibtex
+@article{xie2025play,
+  title     = {Play to Generalize: Learning to Reason Through Game Play},
+  author    = {Xie, Yunfei and Ma, Yinsong and Lan, Shiyi and Yuille, Alan and Xiao, Junfei and Wei, Chen},
+  journal   = {arXiv preprint arXiv:2506.08011},
+  year      = {2025},
+}
+```