Latent Refinement Decoding: Enhancing Diffusion-Based Language Models by
  Refining Belief States
Authors: name: Qinglin Zhu, name: Yizhen Yao, name: Runcong Zhao, name: Yanzheng Xiang, name: Amrutha Saseendran, name: Chen Jin, name: Philip Alexander Teare, name: Bin Liang, name: Yulan He, name: Lin Gui
Organization: King's College London

Autoregressive (AR) models remain the standard for natural language
generation but still suffer from high latency due to strictly sequential
decoding. Recent diffusion-inspired approaches, such as LlaDA and Dream,
mitigate this by generating in parallel, yet they suffer from two core
limitations: information loss, as predictive distributions for non-finalized
tokens are discarded at each step, and premature commitment, where local
decisions are made without sufficient global coordination. We introduce Latent
Refinement Decoding (LRD), a two-stage framework with Latent Refinement and a
Predictive Feedback Loop. The first stage maintains masked positions as
distributional mixtures of predicted tokens and the mask embedding, allowing
the model to establish more globally consistent beliefs. The second stage
progressively finalizes confident tokens while retaining uncertain ones for
iterative feedback. KL-divergence dynamics provide a principled and reliable
criterion for convergence and early stopping. Experiments across coding
(HumanEval +6.3, MBPP +2.6) and reasoning (GSM8K +2.9, MATH500 +3.8) show that
LRD improves accuracy while delivering speedups of up to 10.6x, making it a
strong and versatile alternative for parallel sequence generation.