Diffusion Language Models in Depth

A technical deep dive for anyone who already knows that diffusion language models generate text by iterative unmasking and wants to know how they are actually built. This path covers the formal machinery: categorical transition matrices and the variational bound that reduces to weighted masked language modelling, the from-scratch and checkpoint-adaptation training routes that carried the paradigm to 8 billion parameters, the approximate caching and confidence-aware decoding that finally made serving competitive, and the multimodal, safety, and evaluation problems that remain open.