On November 2, 2026, KAIST announced a fine-tuning method called CluSTER that cut a 10-hour training run to about 3 hours — a 69.6% time saving — while keeping measured performance nearly unchanged. The trick was not a faster chip, a bigger cluster, or a smaller model. It was choosing better data: the models kept their scores while training on only about 31% of the full dataset.

CluSTER — Clustering-Based Balanced Sampling and Allocation with Data Parallelism for High-Performance Fine-Tuning — was presented in October 2026 at EMNLP 2026 (paper: arXiv 2609.12584, first author Hyunjin Kim, corresponding author Professor Jae-Gil Lee, KAIST School of Computing).…