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The Evolution towards a Hybrid Learning Approach
The chapter explores the transition from pure supervised learning to a blend of supervised and self-supervised learning in AI. It discusses the challenges of data collection and training in the supervised learning era, contrasting it with the advantages of using pre-trained models in the self-supervised learning era. The conversation highlights the importance of supervised fine-tuning for model stability and performance, emphasizing the shift towards combining different forms of supervision to enhance model behavior.