AI RESEARCH
Breaking Block Boundaries: Anchor-based History-stable Decoding for Diffusion Large Language Models
arXiv CS.CL
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ArXi:2604.08964v1 Announce Type: new Diffusion Large Language Models (dLLMs) have recently become a promising alternative to autoregressive large language models (ARMs). Semi-autoregressive (Semi-AR) decoding is widely employed in base dLLMs and advanced decoding strategies due to its superior performance. However, our observations reveal that Semi-AR decoding suffers from inherent block constraints, which cause the decoding of many cross-block stable tokens to be unnecessarily delayed.