Pretraining.

Pretraining is the foundational phase of language model development where models learn to predict the next token in sequences by processing massive amounts of text data, typically measured in trillions of tokens. This self-supervised learning process enables models to acquire broad knowledge about language, facts, reasoning patterns, and cultural understanding without explicit human labeling. The pretraining phase is computationally intensive, often requiring thousands of GPUs running for months and consuming enormous amounts of electricity and water for cooling.

During pretraining, models process diverse text sources including web pages, books, academic papers, and code repositories, learning statistical patterns that allow them to generate coherent and contextually appropriate text. The training objective is deceptively simple: given a sequence of tokens, predict what comes next. However, this process forces models to develop sophisticated internal representations of syntax, semantics, world knowledge, and reasoning capabilities. The quality and diversity of pretraining data significantly influences the model's capabilities, biases, and knowledge cutoffs.

Pretraining creates base models that serve as the foundation for all subsequent specialization through fine-tuning, instruction tuning, and reinforcement learning from human feedback. While pretrained models can generate text, they often require additional training phases to become useful assistants that follow instructions reliably. The pretraining phase represents the most expensive part of model development, with leading models costing tens to hundreds of millions of dollars to train, making it accessible primarily to well-funded organizations with significant computational resources.