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---
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license: apache-2.0
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---
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# MMRL30k: A Diverse Training Dataset for Reinforcement Learning Used by Shuffle-R1
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The training data contains 2.1k samples from Geometry3K and 27k random selected samples from MM-EUREKA dataset. Each sample in the dataset follows the format below:
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```
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{
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"problem": "your problem", # type: str
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"images": [{"bytes": image_bytes, "path": None}], # type: list[dict]
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"answer": "your answer", # type: str
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"source": "data source" # type: str, not used in training
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}
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```
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## Usage
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The training data follows the format of [**EasyR1**](https://github.com/hiyouga/EasyR1).
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Refer to [**Shuffle-R1**](https://github.com/xiaomi-research/shuffle-r1) for training usage.
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## Acknowledgement
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The training data is collected from [**Geometry3K**](https://huggingface.co/datasets/hiyouga/geometry3k) and [**MM-EUREKA dataset**](https://huggingface.co/datasets/FanqingM/MM-Eureka-Dataset)
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## Citation
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If you find our work useful for your research, please consider citing:
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```
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@misc{zhu2025shuffler1,
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title={Shuffle-R1: Efficient RL framework for Multimodal Large Language Models via Data-centric Dynamic Shuffle},
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author={Linghao Zhu, Yiran Guan, Dingkang Liang, Jianzhong Ju, Zhenbo Luo, Bin Qin, Jian Luan, Yuliang Liu, Xiang Bai},
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year={2025},
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eprint={2508.05612},
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archivePrefix={arXiv},
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primaryClass={cs.LG},
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url={https://arxiv.org/abs/2508.05612},
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}
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``` |
