How To Build A Modern Diffusion Language Model

arXiv cs.AIOctober 27, 2026en

arXiv cs.AI

AI Global Wire

arXiv:2610.33391v1 Announce Type: new 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

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