Title: Latent Refinement Decoding: Enhancing Diffusion-Based Language Models by Refining Belief States
Primary area: generative models
Keywords: Diffusion Language Models, Latent Refinement Decoding, Mixture Embedding

Abstract: 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 10.6×, making it a strong and versatile alternative for parallel sequence generation.

TL;DR: We propose Latent Refinement Decoding, a two-stage diffusion decoding framework that reduces information loss and improves accuracy with faster inference.