How to Build a Modern Diffusion Language Model

This talk surveys the components of modern diffusion language models, beginning with masked prediction and extending to iterative refinement, controlled generation and variable-length inference. It examines how parallel generation and repeated revision change inference-time computation. Examples from natural language, proteins and DNA illustrate both practical model designs and uses of fine-grained control in scientific sequence generation.