CreditDecoding: Accelerating Parallel Decoding in Diffusion Large Language Models with Trace Credit 事件

CreditDecoding: Accelerating Parallel Decoding in Diffusion Large Language Models with Trace Credit arXiv:2510.06133v3 Announce Type: replace Abstract: Diffusion large language models (dLLMs) generate text through iterative denoising. In commonly adopted parallel decoding schemes, each step confirms only high-confidence positions while remasking the others. By analyzing dLLM denoising traces, we uncover a key inefficiency: models often predict the correct target token several steps before its c