Rethinking Diffusion Language Models: Can dLLMs Replace Autoregressive Models?
mblondel_ml · x · 2026-08-10
Junbo Zhao published an essay reviewing the evolution of diffusion language models (dLLMs) over the past year, from LLaDA-MoE to LLaDA 2.2, exploring their core advantages and future ecosystem positioning compared to autoregressive (AR) models.
In a comment, researcher Mathieu Blondel pointed out that many advantages of dLLMs—such as editing, infilling, and structured generation—are actually achievable by thinking AR models. The key difference is that dLLMs can perform these modifications in place, while AR models rely on a "tape" mechanism using a larger context window.
Related event: Diffusion LLMs: Progress and Prospects(2 posts)→
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