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Research questionHow can masked diffusion language models retain generation quality with only a few denoising steps?Few-step generation is difficult because fully masked trajectories discard terminal entropy, while alternatives can blur the distinction between clean tokens and noise. These compromises can reduce both generated-text quality and training efficiency.
AI
Code Generation & Program Synthesis
Diffusion Models
Inference Optimization
Machine Learning
Natural Language Processing
Reasoning
Research Paper
Latest papersRecent research connected to this question, newest first.Follow the Latent Roadmap: Navigating Revocable Decoding for Diffusion LLMs with Anchor TokensApplies to diffusion LLMs performing parallel generation with revocable decoding. The source presents a training-free embedding-space framework using temporally consistent anchor tokens and evaluates it on mathematical reasoning and code-generation benchmarks; reported gains are benchmark-specific.research paper · Sep 2, 2026Multi-Mask Diffusion Language Models for Few-Step GenerationThe source studies masked diffusion language models for few-step text generation, including pretraining and discrete-state consistency distillation. It reports experiments on pretraining and distillation and derives an ELBO objective that supports continued training from pretrained masked diffusion models.research paper · Sep 2, 2026
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