Looped Uniform Diffusion Language Models

Saiteja Utpala, Chieh-Hsin Lai, Yuki Mitsufuji, Praneeth Vepakomma · 2026 · arXiv

DOI: 10.48550/arXiv.2610.30066 · open-access PDF

Arabic

يتم إنتاج نموذج لغة التفكير المشترك (UDLM) من خلال التفكير المشترك من خلال التفكير المشترك من خلال التفكير المشترك من خلال التفكير المشترك من خلال التفكير المشترك من خلال التفكير المشترك من خلال التفكير المشترك من خلال التفكير المشترك من خلال التفكير المشترك من خلال التفكير المشترك من خلال التفكير المشترك.

Machine translation (Meta M2M100); the English original follows.

Abstract

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

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