Looped Uniform Diffusion Language Models
Uniform diffusion language models (UDLMs) generate text by iteratively denoising sequences of ordinary vocabulary tokens, any of which may be corrupted and subsequently revised. During training, UDLMs must denoise from a context that may itself be unreliable, whereas masked diffusion language models (MDLMs) always condition on a clean context. This challenging training objective makes UDLMs more FLOP-intensive to train than MDLMs, but it yields stronger downstream reasoning performance. Recently, selective looping, which recursively applies a few early Transformer layers within each denoising step, has been shown to improve training efficiency in MDLMs under an iso-FLOP budget. We investi...
| Authors | Saiteja Utpala, Chieh-Hsin Lai, Yuki Mitsufuji et al. |
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| Primary category | cs.CL |
| Published | 21 October 2026 |
| Last updated at source | 21 October 2026 |
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