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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},
}
``` |