@inproceedings{wang-etal-2026-less,
title = "Less Uniform Discrete Diffusion is More Powerful and Scalable",
author = "Wang, Kaibo and
Ding, Ding and
Ding, Fangyu and
Feng, Zijin and
Shi, Han and
Bai, Haoli and
Sun, Jiacheng and
Xiang, Yang",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.1714/",
pages = "39089--39109",
ISBN = "979-8-89176-459-0",
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 3 token-per-step speedup over AR decoding and a competitive performance compared with masked diffusion baselines, revealing that the full potential of UDLMs for complex generation remains to be unlocked."
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<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 3 token-per-step speedup over AR decoding and a competitive performance compared with masked diffusion baselines, revealing that the full potential of UDLMs for complex generation remains to be unlocked.</abstract>
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%0 Conference Proceedings
%T Less Uniform Discrete Diffusion is More Powerful and Scalable
%A Wang, Kaibo
%A Ding, Ding
%A Ding, Fangyu
%A Feng, Zijin
%A Shi, Han
%A Bai, Haoli
%A Sun, Jiacheng
%A Xiang, Yang
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F wang-etal-2026-less
%X 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 3 token-per-step speedup over AR decoding and a competitive performance compared with masked diffusion baselines, revealing that the full potential of UDLMs for complex generation remains to be unlocked.
%U https://aclanthology.org/2026.emnlp-main.1714/
%P 39089-39109
Markdown (Informal)
[Less Uniform Discrete Diffusion is More Powerful and Scalable](https://aclanthology.org/2026.emnlp-main.1714/) (Wang et al., EMNLP 2026)
ACL
- Kaibo Wang, Ding Ding, Fangyu Ding, Zijin Feng, Han Shi, Haoli Bai, Jiacheng Sun, and Yang Xiang. 2026. Less Uniform Discrete Diffusion is More Powerful and Scalable. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 39089–39109, Budapest, Hungary. Association for Computational Linguistics.