Work

PURE: Post-hoc Unlocking and REfinement for Discrete Diffusion Decoding

Year: 2026

Type: conference-paper

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 (∆ 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.A Results on LLaDA-1.5

Source: Findings of the Association for Computational Linguistics: ACL (Findings of ACL)

DOI: 10.18653/v1/2026.findings-acl.1234