Traditional autoregressive language models generate text one word – or token – at a time. This sequential process can be slow, and limit the quality and coherence of the output.

Diffusion models work differently. Instead of predicting text directly, they learn to generate outputs by refining noise, step-by-step. This means they can iterate on a solution very quickly and error correct during the generation process. This helps them excel at tasks like editing, including in the context of math and code.