Improve dataset card: Add task categories, links, tags, and data download instructions

README.md

@@ -1,3 +1,47 @@
----
-license: mit
----
+---
+license: mit
+task_categories:
+- text-generation
+language:
+- en
+tags:
+- diffusion-models
+- reinforcement-learning
+- math-reasoning
+- code-generation
+- reasoning
+---
+
+# Revolutionizing Reinforcement Learning Framework for Diffusion Large Language Models Datasets
+
+This repository contains datasets used in the paper [Revolutionizing Reinforcement Learning Framework for Diffusion Large Language Models](https://huggingface.co/papers/2509.06949). These datasets are crucial for building, training, and deploying Diffusion Large Language Models (DLMs) within the TraceRL framework, particularly for improving reasoning performance on complex math and coding tasks.
+
+-   **Paper:** [Revolutionizing Reinforcement Learning Framework for Diffusion Large Language Models](https://huggingface.co/papers/2509.06949)
+-   **Code (GitHub):** [https://github.com/Gen-Verse/dLLM-RL](https://github.com/Gen-Verse/dLLM-RL)
+-   **Project Page (Hugging Face Collection):** [https://huggingface.co/collections/Gen-Verse/trado-series-68beb6cd6a26c27cde9fe3af](https://huggingface.co/collections/Gen-Verse/trado-series-68beb6cd6a26c27cde9fe3af)
+
+## Sample Usage (Data Download)
+
+You can navigate to the `./data` directory within the associated GitHub repository to download datasets for evaluation and training. In that directory, you will also find detailed instructions on how to modify your own dataset.
+
+For example, to download the `MATH500` and `MATH_train` datasets:
+
+```bash
+cd data
+python download_data.py --dataset MATH500
+python download_data.py --dataset MATH_train
+cd ..
+```
+
+## Citation
+
+If you use these datasets in your research, please cite the associated paper:
+
+```bibtex
+@article{wang2025trado,
+  title={Revolutionizing Reinforcement Learning Framework for Diffusion Large Language Models},
+  author={Wang, Yinjie and Yang, Ling and Li, Bowen and Tian, Ye and Shen, Ke and Wang, Mengdi},
+  journal={arXiv preprint arXiv:2509.06949},
+  year={2025}
+}
+```