BinaryOPD-Data / 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?. 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.