@inproceedings{xu-etal-2026-predict,
title = "Predict, Don{'}t Iterate: Efficient Adaptive-Length Infilling for Diffusion Language Models",
author = "Xu, Haobo and
Chen, Sirui and
Bei, Yuanchen and
Chen, Lingjie and
Yan, Yuchen and
Fu, Dongqi and
He, Jingrui and
Tong, Hanghang",
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.1053/",
pages = "24171--24190",
ISBN = "979-8-89176-459-0",
abstract = "Diffusion language models (DLMs) have emerged as a promising alternative to the auto-regressive paradigm. With bidirectional attention and any-order generation, DLMs naturally fit infilling tasks, which require generating a middle span conditioned on both the prefix and the suffix. However, infilling is sensitive to the length of the span, while DLMs require the length to be fixed before generation. Although prior studies extend DLMs to dynamic lengths, they still suffer from two limitations. (i) *Sensitivity to initial length.* These methods require a preset length to initialize the search and are highly sensitive to this initial length, often yielding suboptimal results. (ii) *Inference inefficiency.* They either insert length-changing operations during generation or repeatedly search for an appropriate length using multi-step denoising confidence, both of which introduce substantial additional forward passes and computational cost. Therefore, we propose PILL (**P**robing-based **I**nfi**L**ing with preset-**L**ength-free decoding), an efficient infilling method for DLMs that requires no preset initial length and drastically reduces the number of forward passes compared to baselines. Experiments show that, across five DLMs spanning different families, architectures, and training recipes on eight infilling benchmarks, PILL improves over the strongest baseline by $+4.8$ average pass rate on code and $+6.0$ BLEU-2 on text, while running $1.82\times$ faster than that baseline."
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<abstract>Diffusion language models (DLMs) have emerged as a promising alternative to the auto-regressive paradigm. With bidirectional attention and any-order generation, DLMs naturally fit infilling tasks, which require generating a middle span conditioned on both the prefix and the suffix. However, infilling is sensitive to the length of the span, while DLMs require the length to be fixed before generation. Although prior studies extend DLMs to dynamic lengths, they still suffer from two limitations. (i) *Sensitivity to initial length.* These methods require a preset length to initialize the search and are highly sensitive to this initial length, often yielding suboptimal results. (ii) *Inference inefficiency.* They either insert length-changing operations during generation or repeatedly search for an appropriate length using multi-step denoising confidence, both of which introduce substantial additional forward passes and computational cost. Therefore, we propose PILL (**P**robing-based **I**nfi**L**ing with preset-**L**ength-free decoding), an efficient infilling method for DLMs that requires no preset initial length and drastically reduces the number of forward passes compared to baselines. Experiments show that, across five DLMs spanning different families, architectures, and training recipes on eight infilling benchmarks, PILL improves over the strongest baseline by +4.8 average pass rate on code and +6.0 BLEU-2 on text, while running 1.82\times faster than that baseline.</abstract>
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%0 Conference Proceedings
%T Predict, Don’t Iterate: Efficient Adaptive-Length Infilling for Diffusion Language Models
%A Xu, Haobo
%A Chen, Sirui
%A Bei, Yuanchen
%A Chen, Lingjie
%A Yan, Yuchen
%A Fu, Dongqi
%A He, Jingrui
%A Tong, Hanghang
%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 xu-etal-2026-predict
%X Diffusion language models (DLMs) have emerged as a promising alternative to the auto-regressive paradigm. With bidirectional attention and any-order generation, DLMs naturally fit infilling tasks, which require generating a middle span conditioned on both the prefix and the suffix. However, infilling is sensitive to the length of the span, while DLMs require the length to be fixed before generation. Although prior studies extend DLMs to dynamic lengths, they still suffer from two limitations. (i) *Sensitivity to initial length.* These methods require a preset length to initialize the search and are highly sensitive to this initial length, often yielding suboptimal results. (ii) *Inference inefficiency.* They either insert length-changing operations during generation or repeatedly search for an appropriate length using multi-step denoising confidence, both of which introduce substantial additional forward passes and computational cost. Therefore, we propose PILL (**P**robing-based **I**nfi**L**ing with preset-**L**ength-free decoding), an efficient infilling method for DLMs that requires no preset initial length and drastically reduces the number of forward passes compared to baselines. Experiments show that, across five DLMs spanning different families, architectures, and training recipes on eight infilling benchmarks, PILL improves over the strongest baseline by +4.8 average pass rate on code and +6.0 BLEU-2 on text, while running 1.82\times faster than that baseline.
%U https://aclanthology.org/2026.emnlp-main.1053/
%P 24171-24190
Markdown (Informal)
[Predict, Don’t Iterate: Efficient Adaptive-Length Infilling for Diffusion Language Models](https://aclanthology.org/2026.emnlp-main.1053/) (Xu et al., EMNLP 2026)
ACL
- Haobo Xu, Sirui Chen, Yuanchen Bei, Lingjie Chen, Yuchen Yan, Dongqi Fu, Jingrui He, and Hanghang Tong. 2026. Predict, Don’t Iterate: Efficient Adaptive-Length Infilling for Diffusion Language Models. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 24171–24190, Budapest, Hungary. Association for Computational Linguistics.