80 lines
3.9 KiB
Markdown
80 lines
3.9 KiB
Markdown
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---
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license: apache-2.0
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datasets:
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- Senqiao/VisionThink-Smart-Train
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- Senqiao/VisionThink-Smart-Val
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base_model:
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- Qwen/Qwen2.5-VL-7B-Instruct
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---
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<p align="center" width="100%">
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<img src="https://raw.githubusercontent.com/dvlab-research/VisionThink/main/files/VisionThink.jpg" alt="Stanford-Alpaca" style="width: 100%; min-width: 300px; display: block; margin: auto;">
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</p>
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# VisionThink: Smart and Efficient Vision Language Model via Reinforcement Learning
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[](https://arxiv.org/abs/2507.13348)
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[](https://huggingface.co/papers/2507.13348)
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[](https://github.com/dvlab-research/VisionThink/blob/main/LICENSE)
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<a href='https://huggingface.co/collections/Senqiao/visionthink-6878d839fae02a079c9c7bfe'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Data%20Model-Collection-red'></a>
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## Senqiao/VisionThink-Efficient
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This model is trained via reinforcement learning using [`Senqiao/VisionThink-Smart-Train`](https://huggingface.co/datasets/Senqiao/VisionThink-Smart-Train), demonstrating enhanced performance and efficiency on general VQA tasks.
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**VisionThink: Smart and Efficient Vision Language Model via Reinforcement Learning [[Paper](https://arxiv.org/abs/2507.13348)]** <br />
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[Senqiao Yang](https://scholar.google.com/citations?user=NcJc-RwAAAAJ),
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[Junyi Li](https://scholar.google.com/citations?hl=zh-CN&user=zQ0P3JAAAAAJ),
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[Xin Lai](https://scholar.google.com/citations?user=tqNDPA4AAAAJ),
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[Bei Yu](https://scholar.google.com/citations?user=tGneTm4AAAAJ),
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[Hengshuang Zhao](https://scholar.google.com/citations?user=4uE10I0AAAAJ),
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[Jiaya Jia](https://scholar.google.com/citations?user=XPAkzTEAAAAJ)<br />
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## Highlights
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<p align="center" width="80%">
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<img src="https://raw.githubusercontent.com/dvlab-research/VisionThink/main/files/Framework.jpg" alt="Stanford-Alpaca" style="width: 80%; min-width: 300px; display: block; margin: auto;">
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</p>
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1. Our VisionThink leverages reinforcement learning to **autonomously** learn whether to reduce visual tokens. Compared to traditional efficient VLM approaches, our method achieves significant improvements on **fine-grained** benchmarks, such as those involving OCR-related tasks.
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2. VisionThink improves performance on **General VQA** tasks while reducing visual tokens by **50%**, achieving **102%** of the original model’s performance across nine benchmarks.
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3. VisionThink achieves strong performance and efficiency by simply resizing input images to reduce visual tokens. We hope this inspires further research into **Efficient Reasoning Vision Language Models**.
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## Video
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<p align="center" width="85%">
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<a href="https://www.youtube.com/watch?v=DGjbFbA5mBw" target="_blank">
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<img src="https://raw.githubusercontent.com/dvlab-research/VisionThink/main/files/Video.png" alt="Stanford-Alpaca" style="width: 70%; min-width: 300px; display: block; margin: auto;">
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</a>
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</p>
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## Citation
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If you find this project useful in your research, please consider citing:
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> This work is highly motivated by our previous effort on efficient VLMs, [**VisionZip**](https://github.com/dvlab-research/VisionZip), which explores token compression for faster inference.
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```
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@article{yang2025visionthink,
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title={VisionThink: Smart and Efficient Vision Language Model via Reinforcement Learning},
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author={Yang, Senqiao and Li, Junyi and Lai, Xin and Yu, Bei and Zhao, Hengshuang and Jia, Jiaya},
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journal={arXiv preprint arXiv:2507.13348},
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year={2025}
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}
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@article{yang2024visionzip,
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title={VisionZip: Longer is Better but Not Necessary in Vision Language Models},
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author={Yang, Senqiao and Chen, Yukang and Tian, Zhuotao and Wang, Chengyao and Li, Jingyao and Yu, Bei and Jia, Jiaya},
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journal={arXiv preprint arXiv:2412.04467},
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year={2024}
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}
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```
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