MMRL30k: A Diverse Training Dataset for Reinforcement Learning Used by Shuffle-R1

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:

{
    "problem": "your problem",  # type: str
    "images": [{"bytes": image_bytes, "path": None}],  # type: list[dict]
    "answer": "your answer",  # type: str
    "source": "data source"  # type: str, not used in training
}

Usage

The training data follows the format of EasyR1.

Refer to Shuffle-R1 for training usage.

Acknowledgement

The training data is collected from Geometry3K and MM-EUREKA dataset

Citation

If you find our work useful for your research, please consider citing:

@misc{zhu2025shuffler1,
      title={Shuffle-R1: Efficient RL framework for Multimodal Large Language Models via Data-centric Dynamic Shuffle}, 
      author={Linghao Zhu, Yiran Guan, Dingkang Liang, Jianzhong Ju, Zhenbo Luo, Bin Qin, Jian Luan, Yuliang Liu, Xiang Bai},
      year={2025},
      eprint={2508.05612},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2508.05612}, 
}