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Improved Large Language Diffusion Models

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arXiv:2606. 25331v1 Announce Type: new Abstract: Modern large language models are predominantly trained with autoregressive factorization and causal attention.

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Authors: Shen Nie, Qiyang Min, Shaoxuan Xu, Zihao Huang, Yuxuan Song, Yong Shan, Yankai Lin, Wayne Xin Zhao, Chongxuan Li, Ji-Rong Wen
Released: 2026-06-25 (14d ago)
Topic: Related to current project
Relation to my work: Read next because Improved Large Language Diffusion Models overlaps with clean result "LoRA persona trained on <A> alone emits <B> at 23.5% when a co-trained partner learns <A>...<B>, vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: code, strong, rect, arc-c, eval, epochs, token, compare. Source: arxiv cs.CL (NLP).
arXiv: 2606.25331

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