@inproceedings{huang-etal-2026-diffusion,
title = "Diffusion Language Model Inference with {M}onte {C}arlo Tree Search",
author = "Huang, Zheng and
Ramnath, Kiran and
Chen, Yueyan and
Feng, Aosong and
Woo, Sangmin and
Srinivasan, Balasubramaniam and
Xu, Zhichao and
Zhou, Kang and
Wang, Shuai and
Ding, Haibo and
Cheong, Lin Lee",
editor = "Demberg, Vera and
Inui, Kentaro and
Marquez, Llu{\'i}s",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EACL} 2026",
month = mar,
year = "2026",
address = "Rabat, Morocco",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-eacl.180/",
pages = "3493--3512",
ISBN = "979-8-89176-386-9",
abstract = "Diffusion language models (DLMs) have recently emerged as a compelling alternative to autoregressive generation, offering parallel generation and improved global coherence. During inference, DLMs generate text by iteratively denoising masked sequences in parallel; however, determining which positions to unmask and which tokens to commit forms a large combinatorial search problem. Existing inference methods approximate this search using heuristics, which often yield suboptimal decoding paths; other approaches instead rely on additional training to guide token selection. To introduce a principled search mechanism for DLMs inference, we introduce MEDAL, an inference-time scaling framework that integrates Monte Carlo Tree SEarch initialization for Diffusion LAnguage Model inference. We employ Monte Carlo Tree Search at the initialization stage to explore promising unmasking trajectories, providing a robust starting point for subsequent refinement. This design enables efficient inference-time scaling, allowing generation quality to improve as the search budget increases, without additional training. Across multiple benchmarks, MEDAL achieves up to 22.0{\%} improvement over existing inference strategies, establishing a new paradigm for search-based inference in DLMs."
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<abstract>Diffusion language models (DLMs) have recently emerged as a compelling alternative to autoregressive generation, offering parallel generation and improved global coherence. During inference, DLMs generate text by iteratively denoising masked sequences in parallel; however, determining which positions to unmask and which tokens to commit forms a large combinatorial search problem. Existing inference methods approximate this search using heuristics, which often yield suboptimal decoding paths; other approaches instead rely on additional training to guide token selection. To introduce a principled search mechanism for DLMs inference, we introduce MEDAL, an inference-time scaling framework that integrates Monte Carlo Tree SEarch initialization for Diffusion LAnguage Model inference. We employ Monte Carlo Tree Search at the initialization stage to explore promising unmasking trajectories, providing a robust starting point for subsequent refinement. This design enables efficient inference-time scaling, allowing generation quality to improve as the search budget increases, without additional training. Across multiple benchmarks, MEDAL achieves up to 22.0% improvement over existing inference strategies, establishing a new paradigm for search-based inference in DLMs.</abstract>
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%0 Conference Proceedings
%T Diffusion Language Model Inference with Monte Carlo Tree Search
%A Huang, Zheng
%A Ramnath, Kiran
%A Chen, Yueyan
%A Feng, Aosong
%A Woo, Sangmin
%A Srinivasan, Balasubramaniam
%A Xu, Zhichao
%A Zhou, Kang
%A Wang, Shuai
%A Ding, Haibo
%A Cheong, Lin Lee
%Y Demberg, Vera
%Y Inui, Kentaro
%Y Marquez, Lluís
%S Findings of the Association for Computational Linguistics: EACL 2026
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-386-9
%F huang-etal-2026-diffusion
%X Diffusion language models (DLMs) have recently emerged as a compelling alternative to autoregressive generation, offering parallel generation and improved global coherence. During inference, DLMs generate text by iteratively denoising masked sequences in parallel; however, determining which positions to unmask and which tokens to commit forms a large combinatorial search problem. Existing inference methods approximate this search using heuristics, which often yield suboptimal decoding paths; other approaches instead rely on additional training to guide token selection. To introduce a principled search mechanism for DLMs inference, we introduce MEDAL, an inference-time scaling framework that integrates Monte Carlo Tree SEarch initialization for Diffusion LAnguage Model inference. We employ Monte Carlo Tree Search at the initialization stage to explore promising unmasking trajectories, providing a robust starting point for subsequent refinement. This design enables efficient inference-time scaling, allowing generation quality to improve as the search budget increases, without additional training. Across multiple benchmarks, MEDAL achieves up to 22.0% improvement over existing inference strategies, establishing a new paradigm for search-based inference in DLMs.
%U https://aclanthology.org/2026.findings-eacl.180/
%P 3493-3512
Markdown (Informal)
[Diffusion Language Model Inference with Monte Carlo Tree Search](https://aclanthology.org/2026.findings-eacl.180/) (Huang et al., Findings 2026)
ACL
- Zheng Huang, Kiran Ramnath, Yueyan Chen, Aosong Feng, Sangmin Woo, Balasubramaniam Srinivasan, Zhichao Xu, Kang Zhou, Shuai Wang, Haibo Ding, and Lin Lee Cheong. 2026. Diffusion Language Model Inference with Monte Carlo Tree Search. In Findings of the Association for Computational Linguistics: EACL 2026, pages 3493–3512, Rabat, Morocco. Association for Computational Linguistics.