Pretraining Paradigms

Explore the core pretraining paradigms used in foundation models, including autoregressive next-token prediction, masked language modeling, and contrastive learning. Understand how these methods optimize training and influence model inference, enabling versatile applications across text, vision, and speech domains. Gain insight into the workflow and benefits of large-scale self-supervised pretraining.