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Accelerating Masked Diffusion Large Language Models: A Survey of Efficient Inference Techniques

paperactiveprovisionalaccelerating-masked-diffusion-large-language-models-a-survey-of-efficient-inference-techniques-21db1fd6·1 events·first seen 28h ago

Aliases: Accelerating Masked Diffusion Large Language Models: A Survey of Efficient Inference Techniques

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5arXiv · cs.CL·28h ago·source ↗

Survey on efficient inference techniques for masked diffusion large language models

A new arXiv survey introduces a unified latency decomposition framework for diffusion large language models (dLLMs) to systematically analyze inference efficiency. The authors categorize acceleration techniques across three axes: algorithmic innovations, architectural and system optimizations, and inference-time scaling. The paper addresses the gap between dLLMs' theoretical parallel-generation advantage and practical deployment speedups, and provides benchmarking guidelines for reproducible comparisons.

Inference Economics Accelerating Masked Diffusion Large Language Models: A Survey of Efficient Inference Techniques