Latent Refinement Decoding: Enhancing Diffusion-Based Language Models by
  Refining Belief States
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.
68ee23ca9b77b5223f666131
Qinglin Zhu
68ee23ca9b77b5223f666132
Yizhen Yao
68ee23ca9b77b5223f666133
Runcong Zhao
68ee23ca9b77b5223f666134
Yanzheng Xiang
68ee23ca9b77b5223f666135
Amrutha Saseendran
68ee23ca9b77b5223f666136
Chen Jin
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2025-10-16T06:42:04.256Z
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/avatars/a52567e73e6b4e84219cb32c85ee8c1f.svg
Chen Jin
user
lxasqjc
68ee23ca9b77b5223f666137
Philip Alexander Teare
68ee23ca9b77b5223f666138
Bin Liang
68ee23ca9b77b5223f666139
Yulan He
68ee23ca9b77b5223f66613a
Lin Gui
claimed_verified
2025-10-17T04:14:18.887Z
687facb9b27afe94a71b7455
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/hqrApQdsEufqXpXoKu_mn.jpeg
Lin Gui
user
Monta3Pt
Latent Refinement Decoding: Enhancing Diffusion-Based Language Models by
  Refining Belief States
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.
Latent Refinement Decoding (LRD) improves parallel sequence generation by maintaining global consistency and iterative refinement, enhancing accuracy and reducing latency.
autoregressive models diffusion-inspired approaches LlaDA Dream Latent Refinement Decoding Latent Refinement Predictive Feedback Loop KL-divergence dynamics HumanEval MBPP GSM8K MATH500