@inproceedings{park-etal-2026-pure,
title = "{PURE}: Post-hoc Unlocking and {RE}finement for Discrete Diffusion Decoding",
author = "Park, Yangryeol and
Lee, Kunhui and
Choi, Hanback and
Park, Cheoneum and
Jeon, Donghyeon and
Kang, Inho and
Na, Seung-Hoon",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.1234/",
pages = "24653--24667",
ISBN = "979-8-89176-395-1",
abstract = "Masked diffusion language models (MDLMs) enable efficient parallel decoding but are limited by a monotonic unmasking policy, where committed tokens cannot be revised. While remasking-based methods mitigate early errors, they mainly intervene during generation. In this work, we study post-hoc refinement of a completed draft and find that naive correction often fails because of contextual lock-in, a phenomenon in which local error patterns become self-reinforcing. To address this, we propose PURE (Post-hoc Unlocking and REfinement), a training-free inference algorithm for two-phase decoding. PURE profiles confidence dynamics during drafting to identify unstable regions via an instability score ($\Delta_i$), then unlocks them through deterministic window masking and stochastic leftward relaxation. On reasoning benchmarks, PURE substantially improves accuracy when applied to LLaDA-8B-Instruct, including a gain of +12.9 points over the baseline on GSM8K. These gains require only a small refinement budget, yielding a favorable compute-quality trade-off for discrete diffusion decoding."
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<abstract>Masked diffusion language models (MDLMs) enable efficient parallel decoding but are limited by a monotonic unmasking policy, where committed tokens cannot be revised. While remasking-based methods mitigate early errors, they mainly intervene during generation. In this work, we study post-hoc refinement of a completed draft and find that naive correction often fails because of contextual lock-in, a phenomenon in which local error patterns become self-reinforcing. To address this, we propose PURE (Post-hoc Unlocking and REfinement), a training-free inference algorithm for two-phase decoding. PURE profiles confidence dynamics during drafting to identify unstable regions via an instability score (Δ_i), then unlocks them through deterministic window masking and stochastic leftward relaxation. On reasoning benchmarks, PURE substantially improves accuracy when applied to LLaDA-8B-Instruct, including a gain of +12.9 points over the baseline on GSM8K. These gains require only a small refinement budget, yielding a favorable compute-quality trade-off for discrete diffusion decoding.</abstract>
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%0 Conference Proceedings
%T PURE: Post-hoc Unlocking and REfinement for Discrete Diffusion Decoding
%A Park, Yangryeol
%A Lee, Kunhui
%A Choi, Hanback
%A Park, Cheoneum
%A Jeon, Donghyeon
%A Kang, Inho
%A Na, Seung-Hoon
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F park-etal-2026-pure
%X Masked diffusion language models (MDLMs) enable efficient parallel decoding but are limited by a monotonic unmasking policy, where committed tokens cannot be revised. While remasking-based methods mitigate early errors, they mainly intervene during generation. In this work, we study post-hoc refinement of a completed draft and find that naive correction often fails because of contextual lock-in, a phenomenon in which local error patterns become self-reinforcing. To address this, we propose PURE (Post-hoc Unlocking and REfinement), a training-free inference algorithm for two-phase decoding. PURE profiles confidence dynamics during drafting to identify unstable regions via an instability score (Δ_i), then unlocks them through deterministic window masking and stochastic leftward relaxation. On reasoning benchmarks, PURE substantially improves accuracy when applied to LLaDA-8B-Instruct, including a gain of +12.9 points over the baseline on GSM8K. These gains require only a small refinement budget, yielding a favorable compute-quality trade-off for discrete diffusion decoding.
%U https://aclanthology.org/2026.findings-acl.1234/
%P 24653-24667
Markdown (Informal)
[PURE: Post-hoc Unlocking and REfinement for Discrete Diffusion Decoding](https://aclanthology.org/2026.findings-acl.1234/) (Park et al., Findings 2026)
ACL
- Yangryeol Park, Kunhui Lee, Hanback Choi, Cheoneum Park, Donghyeon Jeon, Inho Kang, and Seung-Hoon Na. 2026. PURE: Post-hoc Unlocking and REfinement for Discrete Diffusion Decoding. In Findings of the Association for Computational Linguistics: ACL 2026, pages 24653–24667, San Diego, California, United States. Association for Computational Linguistics.