ArXiv

PPL-Factory: Task-Aware and Budget-Aware Data Selection from Language Modeling to Reasoning

2026-07-20 · 17:38 UTC ·Hang Zhang, Warren J. Gross ·1 min read
Authors
Hang Zhang, Warren J. Gross
Categories
cs.CL, cs.LG
arXiv
https://arxiv.org/abs/2607.18199v1
PDF
https://arxiv.org/pdf/2607.18199v1

Brief

PPL-Factory is a task- and budget-aware data-selection framework that leverages task-specific perplexity scores to estimate sample difficulty and select compact, informative subsets for fine-tuning. The method reduces compute while preserving or improving downstream performance: it beats prior selection methods on GSM8K with 1% of data, and with 10% outperforms full-data fine-tuning by 0.9 (GSM8K) and 4.8 (MATH). Summary based on the abstract.

Why it matters

PPL-Factory combines task-aware perplexity scoring with budget-aware selection to pick informative training samples; on GSM8K it outperforms other state-of-the-art data selection methods using only 1% of the training set.

Key details

  • Using 10% of the data, PPL-Factory exceeds full-data fine-tuning accuracy by 0.9 points on GSM8K and by 4.8 points on MATH (results reported in the abstract).
Source evidence

Abstract

Not all training samples contribute equally to large language model fine-tuning. Selecting informative training samples can reduce the computational cost while preserving downstream performance. Many existing data selection methods rely on indirect heuristics, such as data quality, diversity or reasoning trace length. However, the effectiveness of these fixed criteria is task-dependent and difficult to generalize across diverse downstream tasks. Perplexity-based data selection provides a simple and model-aware solution to estimate the sample difficulty, but existing approaches typically score the entire training sequence and ignore the difference in learning objectives of language modeling and reasoning tasks. In this paper, we propose PPL-Factory, a simple and interpretable data selection framework that combines task-aware perplexity-based scores and data budget-aware selection criteria. Experiments on GSM8K demonstrate that PPL-Factory outperforms other state-of-the-art data selection methods using only $1\%$ of the training set. With $10\%$ of the data, PPL-Factory exceeds full-data fine-tuning accuracy by 0.9 on GSM8K and 4.8 on MATH. Overall, our results demonstrate that task-aware and budget-aware perplexity-based selection provides an effective and applicable approach for efficient fine-tuning.

Comment: 13 pages, 4 figures, 5 tables