Training is the process of teaching a machine learning model to perform its task by repeatedly exposing it to data and adjusting its internal parameters based on how wrong or right its outputs are. For a large language model, this means processing enormous volumes of text, comparing its predictions against the actual next word, and nudging millions or billions of internal weights slightly closer to producing better predictions each pass.
The scale involved for modern models is substantial: training runs can take weeks on thousands of specialized processors and consume enormous amounts of data and compute, which is why training a large model from scratch is expensive and why techniques like fine-tuning, which adapt an already-trained model to a narrower task, are so widely used instead of starting over each time.
