Pretraining

Pretraining is a foundational machine learning process that trains neural networks on large-scale, unlabeled datasets to learn general representations and patterns before fine-tuning for specific downstream tasks. This approach enables models to acquire broad knowledge about language structure, world facts, and reasoning capabilities through self-supervised learning objectives such as next-token prediction or masked language modeling. Pretraining typically requires massive computational resources and diverse text corpora spanning billions of tokens from books, websites, and academic papers to develop comprehensive understanding across domains. The process establishes robust feature representations that can be efficiently adapted to specialized tasks through transfer learning, dramatically reducing training time and data requirements for downstream applications. Modern pretraining strategies incorporate techniques like curriculum learning, gradient checkpointing, and distributed training to optimize performance while managing computational costs and memory constraints effectively.