AI Research
DC-Leap: Training-Free Acceleration of dLLMs via Draft-Guided Contiguous Leaping Decoding
While parallel decoding is central to the efficiency of Diffusion Large Language Models (dLLMs), current strategies are often hindered by overly conservative confidence thresholds. These thresholds, necessitated by the Joint Probability Dependence Error (JPDE), result in redundan...
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ArXiv CS.AI
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ArXiv CS.AI
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This research addresses a key bottleneck in Diffusion Large Language Models (dLLMs) by proposing a training-free method to significantly accelerate inference speeds. By intelligently managing confidence thresholds and integrating draft-guided look-ahead, DC-Leap offers substantial speedups without sacrificing generation quality.
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