Add dataset card with task category and links

README.md

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+---
+task_categories:
+- text-generation
+tags:
+- distillation
+- llm
+- on-policy-distillation
+---
+
+# Do We Really Need KL Divergence for On-Policy Distillation of Large Language Models?
+
+This dataset supports the paper [Do We Really Need KL Divergence for On-Policy Distillation of Large Language Models?](https://huggingface.co/papers/2609.33791). It contains training and evaluation data for on-policy distillation (OPD) experiments, including BinaryOPD, positive/negative reward experiments, and consensus multi-teacher on-policy distillation (C-MOPD).
+
+Code and additional details are available in the [GitHub repository](https://github.com/LeapLabTHU/KL-Free-OPD).
+
+## Dataset Structure
+
+| Split    | File                                    | Content                                                        |
+|----------|-----------------------------------------|----------------------------------------------------------------|
+| `train/` | `DAPO-Math-17k.parquet`                 | DAPO math training set                                         |
+| `train/` | `DeepMath-103K-filtered-level6.parquet` | DeepMath, difficulty ≥ 6                                       |
+| `train/` | `Eurus-RL-Data-code.parquet`            | Eurus code training set                                        |
+| `train/` | `math_and_code.parquet`                 | Balanced DeepMath + Eurus mixture for MOPD/C-MOPD              |
+| `eval/`  | `math_eval.parquet`                     | AIME24/25, AMC, MATH500, Minerva, OlympiadBench, IMO-Bench     |
+| `eval/`  | `livecodebench_v6.parquet`              | LiveCodeBench v6 evaluation slice                              |
+| `eval/`  | `HumanEval.parquet`                     | HumanEval                                                      |
+| `eval/`  | `MBPP.parquet`                          | MBPP                                                           |
+
+## Usage
+
+Please refer to the [GitHub README](https://github.com/LeapLabTHU/KL-Free-OPD) for training scripts, reward variants, multi-teacher distillation experiments, and evaluation instructions.