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metadata
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?. 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.
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 for training scripts, reward variants, multi-teacher distillation experiments, and evaluation instructions.