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README.md

@@ -1,3 +1,40 @@
 ---
 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}, 
+}
+```