How to build a diffusion language model

Diffusion language models represent an alternative approach to autoregressive models for generating text by refining entire sequences in parallel rather than producing tokens one at a time. Unlike autoregressive models, diffusion LLMs can correct errors iteratively, generate faster through parallel processing, and attend to bidirectional context. The field reached maturity in 2024-2026 with competitive models from major labs including Google's Gemma Diffusion, NVIDIA's Nemotron Diffusion, and Inception Labs' Mercury 2.