VisionThink: Smart and Efficient Vision Language Model via Reinforcement Learning

Senqiao/VisionThink-Efficient

Highlights

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.
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.
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.

Video

Citation

This work is highly motivated by our previous effort on efficient VLMs, VisionZip, which explores token compression for faster inference.

@article{yang2025visionthink,
  title={VisionThink: Smart and Efficient Vision Language Model via Reinforcement Learning},
  author={Yang, Senqiao and Li, Junyi and Lai, Xin and Yu, Bei and Zhao, Hengshuang and Jia, Jiaya},
  journal={arXiv preprint arXiv:2507.13348},
  year={2025}
}
@article{yang2024visionzip,
  title={VisionZip: Longer is Better but Not Necessary in Vision Language Models},
  author={Yang, Senqiao and Chen, Yukang and Tian, Zhuotao and Wang, Chengyao and Li, Jingyao and Yu, Bei and Jia, Jiaya},
  journal={arXiv preprint arXiv:2412.04467},
  year={2024}
}