DataFlex: A Unified Framework for Data-Centric Dynamic Training of Large Language Models
DataFlex introduces a unified framework for dynamic data-centric training of LLMs by treating data as an optimization variable. It integrates three core paradigms—data selection, domain mixture adjustment, and sample reweighting—into modular components. This allows researchers to systematically study and deploy dynamic strategies that improve model performance over static training baselines.