AI Generalization: Effective Learning with Less Supervision

In the rapidly evolving landscape of artificial intelligence, a recent study conducted by researchers from Hong Kong University and the University of California, Berkeley, sheds new light on the training methodologies of language models.Traditionally, the belief has been that supervised fine-tuning (SFT) is the most effective approach for developing robust AI systems, heavily relying on meticulously curated training examples.