---
license: apache-2.0
---

# 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**](https://github.com/hiyouga/EasyR1). 

Refer to [**Shuffle-R1**](https://github.com/xiaomi-research/shuffle-r1) for training usage.


## Acknowledgement
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)

## 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}, 
}
```