@inproceedings{mo-etal-2026-decoding,
title = "Decoding Diffusion Language Models with Foreseeing Movement",
author = "Mo, Yichuan and
Chen, Quan and
Li, Mingjie and
Wei, Zeming and
Wang, Yisen",
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.909/",
pages = "20943--20958",
ISBN = "979-8-89176-459-0",
abstract = "Diffusion Language Models (DLMs) benefit from a flexible decoding mechanism that enables parallelized inference and controllable generations over autoregressive models. Yet such flexibility introduces a critical challenge: inference performance becomes highly sensitive to the decoding order of tokens. Existing heuristic methods, however, focus mainly on local effects while overlooking long-term impacts. To address this limitation, we propose the Foreseeing Decoding Method (FDM), a novel approach that integrates both local and global considerations to unlock the full potential, employing a search-based strategy to enable effective optimization in discrete spaces. Furthermore, by analyzing the consistency of chosen tokens in the full decoding process, we develop a variant, FDM with Acceleration (FDM-A), which restricts deep exploration to critical steps identified as the exploration and balance circumstances. Extensive experiments across diverse benchmarks and model architectures validate the scalability of FDM and demonstrate the superior efficiency-performance trade-off achieved by FDM-A. Our work might potentially provide a principled step toward more powerful decoding methods for DLMs. Our code is available at https://github.com/PKU-ML/FDM."
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%0 Conference Proceedings
%T Decoding Diffusion Language Models with Foreseeing Movement
%A Mo, Yichuan
%A Chen, Quan
%A Li, Mingjie
%A Wei, Zeming
%A Wang, Yisen
%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 mo-etal-2026-decoding
%X Diffusion Language Models (DLMs) benefit from a flexible decoding mechanism that enables parallelized inference and controllable generations over autoregressive models. Yet such flexibility introduces a critical challenge: inference performance becomes highly sensitive to the decoding order of tokens. Existing heuristic methods, however, focus mainly on local effects while overlooking long-term impacts. To address this limitation, we propose the Foreseeing Decoding Method (FDM), a novel approach that integrates both local and global considerations to unlock the full potential, employing a search-based strategy to enable effective optimization in discrete spaces. Furthermore, by analyzing the consistency of chosen tokens in the full decoding process, we develop a variant, FDM with Acceleration (FDM-A), which restricts deep exploration to critical steps identified as the exploration and balance circumstances. Extensive experiments across diverse benchmarks and model architectures validate the scalability of FDM and demonstrate the superior efficiency-performance trade-off achieved by FDM-A. Our work might potentially provide a principled step toward more powerful decoding methods for DLMs. Our code is available at https://github.com/PKU-ML/FDM.
%U https://aclanthology.org/2026.emnlp-main.909/
%P 20943-20958
Markdown (Informal)
[Decoding Diffusion Language Models with Foreseeing Movement](https://aclanthology.org/2026.emnlp-main.909/) (Mo et al., EMNLP 2026)
ACL
- Yichuan Mo, Quan Chen, Mingjie Li, Zeming Wei, and Yisen Wang. 2026. Decoding Diffusion Language Models with Foreseeing Movement. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 20943–20958, Budapest, Hungary. Association for Computational Linguistics.