@inproceedings{zhong-etal-2026-beyond,
title = "Beyond Hard Masks: Progressive Token Evolution for Diffusion Language Models",
author = "Zhong, Linhao and
Wu, Linyu and
Fang, Bozhen and
Feng, Tianjian and
Jing, Chenchen and
Wang, Wen and
Zhang, Jiaheng and
Chen, Hao and
Shen, Chunhua",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.315/",
pages = "6936--6959",
ISBN = "979-8-89176-390-6",
abstract = "Diffusion Language Models (DLMs) offer a promising alternative for language modeling by enabling parallel decoding through iterative refinement. However, most DLMs rely on hard binary masking and discrete token assignments, which hinder the revision of early decisions and underutilize intermediate probabilistic representations. In this paper, we propose EvoToken-DLM, a novel diffusion-based language modeling approach that replaces hard binary masks with evolving soft token distributions. EvoToken-DLM enables a progressive transition from masked states to discrete outputs, supporting revisable decoding. To effectively support this evolution, we introduce continuous trajectory supervision, which aligns training objectives with iterative probabilistic updates. Extensive experiments across multiple benchmarks show that EvoToken-DLM consistently achieves superior performance, outperforming strong diffusion-based and masked DLM baselines. Our code is available at https://github.com/aim-uofa/EvoTokenDLM."
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<abstract>Diffusion Language Models (DLMs) offer a promising alternative for language modeling by enabling parallel decoding through iterative refinement. However, most DLMs rely on hard binary masking and discrete token assignments, which hinder the revision of early decisions and underutilize intermediate probabilistic representations. In this paper, we propose EvoToken-DLM, a novel diffusion-based language modeling approach that replaces hard binary masks with evolving soft token distributions. EvoToken-DLM enables a progressive transition from masked states to discrete outputs, supporting revisable decoding. To effectively support this evolution, we introduce continuous trajectory supervision, which aligns training objectives with iterative probabilistic updates. Extensive experiments across multiple benchmarks show that EvoToken-DLM consistently achieves superior performance, outperforming strong diffusion-based and masked DLM baselines. Our code is available at https://github.com/aim-uofa/EvoTokenDLM.</abstract>
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%0 Conference Proceedings
%T Beyond Hard Masks: Progressive Token Evolution for Diffusion Language Models
%A Zhong, Linhao
%A Wu, Linyu
%A Fang, Bozhen
%A Feng, Tianjian
%A Jing, Chenchen
%A Wang, Wen
%A Zhang, Jiaheng
%A Chen, Hao
%A Shen, Chunhua
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F zhong-etal-2026-beyond
%X Diffusion Language Models (DLMs) offer a promising alternative for language modeling by enabling parallel decoding through iterative refinement. However, most DLMs rely on hard binary masking and discrete token assignments, which hinder the revision of early decisions and underutilize intermediate probabilistic representations. In this paper, we propose EvoToken-DLM, a novel diffusion-based language modeling approach that replaces hard binary masks with evolving soft token distributions. EvoToken-DLM enables a progressive transition from masked states to discrete outputs, supporting revisable decoding. To effectively support this evolution, we introduce continuous trajectory supervision, which aligns training objectives with iterative probabilistic updates. Extensive experiments across multiple benchmarks show that EvoToken-DLM consistently achieves superior performance, outperforming strong diffusion-based and masked DLM baselines. Our code is available at https://github.com/aim-uofa/EvoTokenDLM.
%U https://aclanthology.org/2026.acl-long.315/
%P 6936-6959
Markdown (Informal)
[Beyond Hard Masks: Progressive Token Evolution for Diffusion Language Models](https://aclanthology.org/2026.acl-long.315/) (Zhong et al., ACL 2026)
ACL
- Linhao Zhong, Linyu Wu, Bozhen Fang, Tianjian Feng, Chenchen Jing, Wen Wang, Jiaheng Zhang, Hao Chen, and Chunhua Shen. 2026. Beyond Hard Masks: Progressive Token Evolution for Diffusion Language Models. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 6936–6959, San Diego, California, United States. Association for Computational Linguistics.