AI brief
Apple researchers propose a distillation method that uses a discrete flow matching model's multi-step trajectory to train a student model to generate text in fewer steps.
Why it matters: Reducing the number of forward passes needed for text generation could make discrete flow matching models faster to run.
Written by AI from Apple Machine Learning Research's published text.
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“Discrete flow matching generates text by iteratively transforming noise tokens into coherent language, but may require hundreds of forward passes. Distillation uses the multi-step trajectory to train a student to reproduce the process in a few steps.”
A paper, laboratory publication or research report.
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