Distribution Matching Distillation for Continuous Diffusion Language Models
Continuous diffusion language models generate all tokens in parallel, yet high-quality generation can still require hundreds of network evaluations (NFEs). We study how distributional distillation can reduce this cost by exploiting the student's probabilistic token outputs. Our unified formulation connects the student'...
Paul Le Van Kiem, Dario Shariatian, Umut Simsekli et al.
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