@inproceedings{xie-etal-2026-rethinking,
title = "Rethinking Diffusion Large Language Models via Time-aware Weighting and Block-oriented Refinements",
author = "Xie, Zheyong and
Li, Shengrui and
Zhong, Li and
Zhao, Kaiyan and
Shi, Fangcheng and
Wang, Jiakang and
Jin, Huayi and
Luo, Zhaokai and
Zhao, Fei and
Liu, Haifeng and
Ye, Jieying and
Lu, Chonggang and
Huang, Wenxuan and
Liu, Yue and
Meng, Zijie and
Cao, Shaosheng",
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.1836/",
pages = "41778--41792",
ISBN = "979-8-89176-459-0",
abstract = "Diffusion Large Language Models (DLLMs) have recently emerged as a parallel alternative to autoregressive models, offering bidirectional context modeling and parallel decoding. However, developing highly performant DLLMs is hindered by lifecycle challenges, including suboptimal timestep weighting in pre-training, train-inference distribution gaps in post-training, and underutilized caching during inference. To address these obstacles, we introduce ReDLLM, a 30B-parameter Mixture-of-Experts DLLM. It incorporates a time-aware weighting strategy during continual pre-training that scales gradients according to denoising difficulty. Furthermore, we propose two block-oriented refinements: a terminal-block treatment to narrow the train-inference gap, and a block-aware inference pipeline featuring inline block prefill and block-level scheduling to maximize KV-cache reuse and parallelization. Extensive evaluations demonstrate that ReDLLM surpasses existing DLLMs at the same active-parameter scale, achieves generation quality competitive with 100B DLLMs, and attains the highest inference throughput among evaluated models."
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<abstract>Diffusion Large Language Models (DLLMs) have recently emerged as a parallel alternative to autoregressive models, offering bidirectional context modeling and parallel decoding. However, developing highly performant DLLMs is hindered by lifecycle challenges, including suboptimal timestep weighting in pre-training, train-inference distribution gaps in post-training, and underutilized caching during inference. To address these obstacles, we introduce ReDLLM, a 30B-parameter Mixture-of-Experts DLLM. It incorporates a time-aware weighting strategy during continual pre-training that scales gradients according to denoising difficulty. Furthermore, we propose two block-oriented refinements: a terminal-block treatment to narrow the train-inference gap, and a block-aware inference pipeline featuring inline block prefill and block-level scheduling to maximize KV-cache reuse and parallelization. Extensive evaluations demonstrate that ReDLLM surpasses existing DLLMs at the same active-parameter scale, achieves generation quality competitive with 100B DLLMs, and attains the highest inference throughput among evaluated models.</abstract>
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%0 Conference Proceedings
%T Rethinking Diffusion Large Language Models via Time-aware Weighting and Block-oriented Refinements
%A Xie, Zheyong
%A Li, Shengrui
%A Zhong, Li
%A Zhao, Kaiyan
%A Shi, Fangcheng
%A Wang, Jiakang
%A Jin, Huayi
%A Luo, Zhaokai
%A Zhao, Fei
%A Liu, Haifeng
%A Ye, Jieying
%A Lu, Chonggang
%A Huang, Wenxuan
%A Liu, Yue
%A Meng, Zijie
%A Cao, Shaosheng
%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 xie-etal-2026-rethinking
%X Diffusion Large Language Models (DLLMs) have recently emerged as a parallel alternative to autoregressive models, offering bidirectional context modeling and parallel decoding. However, developing highly performant DLLMs is hindered by lifecycle challenges, including suboptimal timestep weighting in pre-training, train-inference distribution gaps in post-training, and underutilized caching during inference. To address these obstacles, we introduce ReDLLM, a 30B-parameter Mixture-of-Experts DLLM. It incorporates a time-aware weighting strategy during continual pre-training that scales gradients according to denoising difficulty. Furthermore, we propose two block-oriented refinements: a terminal-block treatment to narrow the train-inference gap, and a block-aware inference pipeline featuring inline block prefill and block-level scheduling to maximize KV-cache reuse and parallelization. Extensive evaluations demonstrate that ReDLLM surpasses existing DLLMs at the same active-parameter scale, achieves generation quality competitive with 100B DLLMs, and attains the highest inference throughput among evaluated models.
%U https://aclanthology.org/2026.emnlp-main.1836/
%P 41778-41792
Markdown (Informal)
[Rethinking Diffusion Large Language Models via Time-aware Weighting and Block-oriented Refinements](https://aclanthology.org/2026.emnlp-main.1836/) (Xie et al., EMNLP 2026)
ACL
- Zheyong Xie, Shengrui Li, Li Zhong, Kaiyan Zhao, Fangcheng Shi, Jiakang Wang, Huayi Jin, Zhaokai Luo, Fei Zhao, Haifeng Liu, Jieying Ye, Chonggang Lu, Wenxuan Huang, Yue Liu, Zijie Meng, and Shaosheng Cao. 2026. Rethinking Diffusion Large Language Models via Time-aware Weighting and Block-oriented Refinements. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 41778–41792, Budapest, Hungary. Association for Computational Linguistics.