Less Uniform Discrete Diffusion is More Powerful and Scalable

Abstract:Although uniform diffusion language models (UDLMs) represent a promising diffusion paradigm, scaling them remains challenging. We identify the core obstacle as an over-uniform training objective and condition-target confusion during sampling. To address these, we propose Less Uniform Diffusion (LUDI), a novel UDLM framework. Specifically, we (i) introduce a less uniform loss that directs each reverse transition toward the clean token, and (ii) equip the model with per-token time embeddings that supply token-level corruption hints, enabling confidence-based few-step sampling. Experiments across scales show that LUDI yields cleaner supervision and improves few-step generation. We further continue-train a 7B autoregressive model into LUDI-7B, resulting in a UDLM capable of complex reasoning. It achieves a 3-token-per-step speedup over AR decoding and competitive performance compared with masked diffusion baselines, revealing that the full potential of UDLMs for complex generation remains to be unlocked.

Computation and Language (cs.CL); Artificial Intelligence (cs.AI)

arXiv:2609.35817 [cs.CL]

(or arXiv:2609.35817v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2609.35817 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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