Less Uniform Discrete Diffusion is More Powerful and Scalable
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 co...
| Authors | Kaibo Wang, Ding Ding, Fangyu Ding et al. |
|---|---|
| Primary category | cs.CL |
| Published | 21 September 2026 |
| Last updated at source | 21 September 2026 |
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