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LemonTea03/BinaryOPD-Data

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.… See the full description on the dataset page: https://huggingface.co/datasets/LemonTea03/BinaryOPD-Data.

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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.