Autoregressive vs Diffusion: Two Competing Approaches to AI Text Generation

Most large language models today use autoregressive generation, which produces text one token at a time from left to right in a strictly sequential process. Diffusion-based text generation takes a different approach, starting with multiple uncertain or masked positions and iteratively refining them rather than completing tokens in fixed order. This allows diffusion models to potentially process multiple parts of a sequence in parallel, which could lead to faster response times. Models such as LLaDA, Dream-7B, and Mercury are among those exploring this method. Diffusion-based generation remains an emerging technique and has not yet matched the consistency of autoregressive models across all tasks.