Datasets:
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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. |