Code LLM Training › Code LLM Training

Code-generating language models do not simply learn to imitate programming syntax. They learn the reasoning structure of software: how functions are composed, how control flow encodes intent, how identifier names carry semantic weight, and how a snippet of code relates to surrounding context in ways that matter for correctness. Getting a model to reliably generate, complete, and explain code requires rethinking nearly every stage of the training pipeline, from the raw bytes of source files all the way through the optimization objective.

This chapter traces that pipeline in full. You will see why code data requires different curation than natural language, why tokenizers for code need to be designed with identifier structure and indentation in mind, what fill-in-the-middle training is and why it transforms what completion models can do, and what pre-training objectives have proven most effective for code models. By the end, you will understand the mechanics of code LLM training and why each design decision exists.