How To Build A Modern Diffusion Language Model

Authors: Volodymyr Kuleshov , Marianne Arriola , Yair Schiff , Gilad Turok , Guanghan Wang

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Abstract: The frontier of large language model (LLM) performance has advanced rapidly in recent years, fueled by scaling laws for pretraining on large text corpora. However, mainstream LLMs have fundamental limitations: they generate text autoregressively, producing outputs sequentially from left to right one token at a time. This sequential process leaves parallel GPU hardware underutilized during inference--a bottleneck that has grown acute as progress shifts toward scaling post-training and inference-time compute--and commits irreversibly to each token without the ability to revise earlier mistakes. Discrete diffusion models address these limitations by generating tokens in parallel, starting from a noisy or masked sequence and iteratively refining the entire output over a series of denoising steps. This parallel generative process enables speeds of thousands of tokens per second, provides a new axis of inference-time compute scaling, and supports built-in error correction and controllable generation. Over the past two years, discrete diffusion has scaled to billions of parameters across biological sequence modeling and natural language, with open-weights and commercial releases from leading labs. This article provides a survey of key ideas in discrete diffusion and a high-level introduction to how modern diffusion LLMs are built, covering their foundations, core algorithmic and architectural building blocks, applications in biology and language, and implications for future LLM scaling laws.

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Artificial Intelligence (cs.AI)

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arXiv:2610.33391 [cs.AI]

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arXiv:2610.33391v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2610.33391

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arXiv-issued DOI via DataCite (pending registration)

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From: Volodymyr Kuleshov [ view email ]

[v1]

Sat, 24 Oct 2026 01:50:31 UTC (4,460 KB)

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