Thyme-SFT

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<img src="https://cdn-uploads.huggingface.co/production/uploads/685ba798484e3233f5ff6f11/dxBp6TmwqwNuBuJR9gfQC.png" width="40%" alt="Thyme Logo">
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[[📖 Home Page](https://thyme-vl.github.io/)]
[[📖 Github Repo](https://github.com/yfzhang114/Thyme)]
[[📖 Technique Report](https://arxiv.org/abs/2508.11630)]

[[📊 Thyme SFT Model](https://huggingface.co/Kwai-Keye/Thyme-SFT)]
[[📊 Thyme RL Model](https://huggingface.co/Kwai-Keye/Thyme-RL)]
[[📝 SFT Data](https://huggingface.co/datasets/Kwai-Keye/Thyme-SFT)]
[[📝 RL Data](https://huggingface.co/datasets/Kwai-Keye/Thyme-RL)]
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## 🔥 News
* **`2025.08.15`** 🌟 We are excited to introduce **Thyme: Think Beyond Images**. Thyme transcends traditional ``thinking with images'' paradigms by autonomously generating and executing diverse image processing and computational operations through executable code, significantly enhancing performance on high-resolution perception and complex reasoning tasks. Leveraging a novel two-stage training strategy that combines supervised fine-tuning with reinforcement learning and empowered by the innovative GRPO-ATS algorithm, Thyme achieves a sophisticated balance between reasoning exploration and code execution precision.

<div align="center">
<img src="https://cdn-uploads.huggingface.co/production/uploads/685ba798484e3233f5ff6f11/c_D7uX3RT1WUANDRB70ZC.png" width="100%" alt="Thyme Logo">
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We have provided the usage instructions, training code, and evaluation code in the [GitHub repo](https://github.com/yfzhang114/Thyme).

## Citation

If you find Thyme useful in your research or applications, please cite our paper:

```bibtex
@misc{zhang2025thymethinkimages,
title={Thyme: Think Beyond Images},
author={Yi-Fan Zhang and Xingyu Lu and Shukang Yin and Chaoyou Fu and Wei Chen and Xiao Hu and Bin Wen and Kaiyu Jiang and Changyi Liu and Tianke Zhang and Haonan Fan and Kaibing Chen and Jiankang Chen and Haojie Ding and Kaiyu Tang and Zhang Zhang and Liang Wang and Fan Yang and Tingting Gao and Guorui Zhou},
year={2025},
eprint={2508.11630},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2508.11630},
}
```

<div align="center">
<img src="https://cdn-uploads.huggingface.co/production/uploads/685ba798484e3233f5ff6f11/dxBp6TmwqwNuBuJR9gfQC.png" width="40%" alt="Thyme Logo">
</div>

<font size=4><div align='center'>
[[📖 主页](https://thyme-vl.github.io/)]
[[📖 GitHub仓库](https://github.com/yfzhang114/Thyme)]
[[📖 技术报告](https://arxiv.org/abs/2508.11630)]

[[📊 Thyme监督微调（SFT）模型](https://huggingface.co/Kwai-Keye/Thyme-SFT)]
[[📊 Thyme强化学习（RL）模型](https://huggingface.co/Kwai-Keye/Thyme-RL)]
[[📝 监督微调数据集](https://huggingface.co/datasets/Kwai-Keye/Thyme-SFT)]
[[📝 强化学习数据集](https://huggingface.co/datasets/Kwai-Keye/Thyme-RL)]
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## 🔥 最新动态
* **`2025.08.15`** 🌟 我们欣喜地推出**Thyme：超越图像的思考者**。Thyme突破了传统"依托图像进行思考"的范式，能够通过可执行代码自主生成并执行多样化的图像处理与计算操作，显著提升了高分辨率感知与复杂推理任务的性能。该模型采用了创新的两阶段训练策略，结合监督微调与强化学习，并依托新颖的GRPO-ATS算法，在推理探索与代码执行精度之间实现了精妙的平衡。

<div align="center">
<img src="https://cdn-uploads.huggingface.co/production/uploads/685ba798484e3233f5ff6f11/c_D7uX3RT1WUANDRB70ZC.png" width="100%" alt="Thyme Logo">
</div>

我们已在[GitHub仓库](https://github.com/yfzhang114/Thyme)中提供了使用说明、训练代码与评估代码。

## 引用
若您在研究或应用中使用了Thyme，请引用我们的论文：
bibtex
@misc{zhang2025thymethinkimages,
title={Thyme: Think Beyond Images},
author={Yi-Fan Zhang and Xingyu Lu and Shukang Yin and Chaoyou Fu and Wei Chen and Xiao Hu and Bin Wen and Kaiyu Jiang and Changyi Liu and Tianke Zhang and Haonan Fan and Kaibing Chen and Jiankang Chen and Haojie Ding and Kaiyu Tang and Zhang Zhang and Liang Wang and Fan Yang and Tingting Gao and Guorui Zhou},
year={2025},
eprint={2508.11630},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2508.11630},
}

搜集汇总

背景与挑战

背景概述

Thyme-SFT数据集专注于通过可执行代码进行图像处理和复杂推理，采用两阶段训练策略（监督微调+强化学习）和GRPO-ATS算法，旨在提升高分辨率感知和复杂任务的处理能力。