Overview of text diffusion models: how masking-based diffusion works, where it differs from autoregressive LLMs, and when it can offer speed.

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Text diffusion models are LLMs that generate text by iteratively denoising masked tokens in parallel, rather than predicting one token at a time like autoregressive models. The most effective current approach uses discrete token masking (as in LLaDA and SEDD) rather than Gaussian noise, since text is categorical data. During inference, a full sequence of MASK tokens is initialized and predicted in parallel across multiple rounds, with low-confidence tokens re-masked for refinement. Key advantages include faster throughput for long outputs, the ability to revise previously generated tokens, and support for gap-fill tasks like completing or editing middle sections of documents. However, text diffusion models are unlikely to replace autoregressive LLMs broadly due to higher compute requirements and generally lower benchmark performance outside specific use cases. LLaDA 2.0 Flash demonstrates competitive benchmark scores with notably higher token throughput (380+ TPS) compared to models like Qwen3-30B.

What are Text Diffusion Models? - An Overview

How Diffusion Models are Architecturally Different