@inproceedings{shen-etal-2026-improving-diffusion,
title = "Improving Diffusion Language Model Decoding through Joint Search in Generation Order and Token Space",
author = "Shen, Yangyi and
Feng, Tianjian and
Han, Jiaqi and
Wang, Wen and
Chen, Tianlang and
Shen, Chunhua and
Leskovec, Jure and
Ermon, Stefano",
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.24/",
pages = "513--532",
ISBN = "979-8-89176-459-0",
abstract = "Diffusion Language Models (DLMs) offer order-agnostic generation that can explore many possible decoding trajectories. However, current decoding methods commit to a single trajectory, limiting exploration in trajectory space. We introduce Order-Token Search to explore this space through jointly searching over generation order and token values. Its core is a likelihood estimator that scores denoising actions, enabling stable pruning and efficient exploration of diverse trajectories. Across mathematical reasoning and coding benchmarks, Order-Token Search consistently outperforms baselines on GSM8K, MATH500, Countdown, and HumanEval (3.1{\%}, 3.8{\%}, 7.9{\%}, and 6.8{\%} absolute over backbone), reaching accuracy comparable to diffu-GRPO post-training. Our work establishes joint search as a key component for advancing decoding in DLMs."
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<abstract>Diffusion Language Models (DLMs) offer order-agnostic generation that can explore many possible decoding trajectories. However, current decoding methods commit to a single trajectory, limiting exploration in trajectory space. We introduce Order-Token Search to explore this space through jointly searching over generation order and token values. Its core is a likelihood estimator that scores denoising actions, enabling stable pruning and efficient exploration of diverse trajectories. Across mathematical reasoning and coding benchmarks, Order-Token Search consistently outperforms baselines on GSM8K, MATH500, Countdown, and HumanEval (3.1%, 3.8%, 7.9%, and 6.8% absolute over backbone), reaching accuracy comparable to diffu-GRPO post-training. Our work establishes joint search as a key component for advancing decoding in DLMs.</abstract>
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%0 Conference Proceedings
%T Improving Diffusion Language Model Decoding through Joint Search in Generation Order and Token Space
%A Shen, Yangyi
%A Feng, Tianjian
%A Han, Jiaqi
%A Wang, Wen
%A Chen, Tianlang
%A Shen, Chunhua
%A Leskovec, Jure
%A Ermon, Stefano
%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 shen-etal-2026-improving-diffusion
%X Diffusion Language Models (DLMs) offer order-agnostic generation that can explore many possible decoding trajectories. However, current decoding methods commit to a single trajectory, limiting exploration in trajectory space. We introduce Order-Token Search to explore this space through jointly searching over generation order and token values. Its core is a likelihood estimator that scores denoising actions, enabling stable pruning and efficient exploration of diverse trajectories. Across mathematical reasoning and coding benchmarks, Order-Token Search consistently outperforms baselines on GSM8K, MATH500, Countdown, and HumanEval (3.1%, 3.8%, 7.9%, and 6.8% absolute over backbone), reaching accuracy comparable to diffu-GRPO post-training. Our work establishes joint search as a key component for advancing decoding in DLMs.
%U https://aclanthology.org/2026.emnlp-main.24/
%P 513-532
Markdown (Informal)
[Improving Diffusion Language Model Decoding through Joint Search in Generation Order and Token Space](https://aclanthology.org/2026.emnlp-main.24/) (Shen et al., EMNLP 2026)
ACL
- Yangyi Shen, Tianjian Feng, Jiaqi Han, Wen Wang, Tianlang Chen, Chunhua Shen, Jure Leskovec, and Stefano Ermon. 2026. Improving Diffusion Language Model Decoding through Joint Search in Generation Order and Token Space. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 513–532, Budapest, Hungary. Association for Computational Linguistics.