DataFlex: A Unified Framework for Data-Centric Dynamic Training of Large Language Models

Excerpt

arXiv:2603.26164v2 Announce Type: replace Abstract: Data-centric training has emerged as a promising direction for improving large language models (LLMs) by optimizing not only model parameters but also the selection, composition, and weighting of training data during optimization. However, existing approaches to data selection, data mixture optimization, and data reweighting are often developed in isolated codebases with inconsistent interfaces, hindering reproducibility, fair comparison, and p