How Are Large Language Models Pretrained? Data, Objectives, and Compute

Large language model pretraining involves optimizing neural networks on extensive token sequences to learn linguistic and factual patterns before specific task adaptation. This process includes data curation, tokenization, sequence sampling, prediction via self-supervision, distributed training, and evaluation. The article clarifies the distinction between pretraining and instruction tuning, emphasizing operational boundaries and system context.