Pre-training is the initial phase of training a large language model on a massive, diverse text corpus — often hundreds of billions of tokens — so it learns general language patterns, world knowledge, and reasoning abilities. The model is typically trained with a next-token prediction objective. Pre-training is extremely expensive (millions of dollars in compute) but produces a foundation model that can be efficiently fine-tuned for specific tasks afterward. GPT, BERT, LLaMA, and Claude all undergo pre-training.