Enhancing Diffusion Language Models with Autoregressive Post-Training Weights
Diffusion language models (dLLMs) have emerged as a promising alternative to autoregressive (AR) language models, offering flexible token-update orders and parallel decoding. Recent dLLMs are often initialized from pretrained AR models before diffusion conversion in order to inherit their learned representations. After...
Yi-Ming Qin, Ke Wang, Amel Abdelraheem et al.
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