Training a specialist AI model is expensive mostly because of repetition. Researchers at KAIST say they have found a way to cut that repetition: a method called CluSTER fine-tuned a model on medical question-and-answer data using about 31 percent of the dataset and cut training time by 69.6 percent, with only a small loss in accuracy, Aju Press reported on November 2, 2026. The problem: fine-tuning wastes GPU time Large language models first learn broad knowledge, then developers fine-tune them...