Engineering Manager, Meta
2 papers at NeurIPS 2025
We present Group-MATES, an efficient group-level data selection method that optimizes the speed-quality frontier for LLM pretraining. On DCLM, Group-MATES achieves up to 9.4% relative accuracy gain and 1.75× faster training than random selection.