39 lines
1.7 KiB
Markdown
39 lines
1.7 KiB
Markdown
---
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base_model:
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- Qwen/Qwen2.5-VL-7B-Instruct
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language:
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- en
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license: apache-2.0
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pipeline_tag: image-text-to-text
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tags:
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- transformers
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- multimodal
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library_name: transformers
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---
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## Model Overview
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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.
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**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.
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## Resources
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For details of our approach and performance comparison, please see our [paper](https://arxiv.org/abs/2506.08011).
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For details of training and evaluation, please see our [code repo](https://github.com/yunfeixie233/ViGaL).
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| [**🚀 Project Page**](https://yunfeixie233.github.io/ViGaL/) | [**📖 Paper**](https://arxiv.org/abs/2506.08011) | [**🔗 GitHub**](https://github.com/yunfeixie233/ViGaL) | [**🤗 Training Data**](https://huggingface.co/yunfeixie/vigal_data) | [**🤗 Model**](https://huggingface.co/yunfeixie/ViGaL-7B) |
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## Citation
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If you feel this model useful, please give us a free cite:
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```bibtex
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@article{xie2025play,
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title = {Play to Generalize: Learning to Reason Through Game Play},
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author = {Xie, Yunfei and Ma, Yinsong and Lan, Shiyi and Yuille, Alan and Xiao, Junfei and Wei, Chen},
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journal = {arXiv preprint arXiv:2506.08011},
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year = {2025},
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}
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``` |