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Discrete Diffusion in Large Language and Multimodal Models: A Survey · Around

2025

Discrete Diffusion in Large Language and Multimodal Models: A Survey

Yu, Runpeng, Li, Qi, Wang, Xinchao

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In this work, we provide a systematic survey of Discrete Diffusion Language Models (dLLMs) and Discrete Diffusion Multimodal Language Models (dMLLMs).

Unlike autoregressive (AR) models, dLLMs and dMLLMs adopt a multi-token, parallel decoding paradigm using full attention and a denoising-based generation strategy.
This paradigm naturally enables parallel generation, fine-grained output control, and dynamic perception.
These capabilities are previously difficult to achieve with AR models.

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