Apple Researchers Propose Energy-Navigated Distillation for Few-Step Discrete Flow Matching
Apple researchers argue that in distilling discrete flow matching models, the multi-step training trajectory, not student capacity, is the bottleneck. Each trajectory is built through blind stochastic jumps with no sequence-quality evaluation, so one bad early decision propagates. They propose energy-navigated distillation to train students to reproduce the process in few steps.
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“We argue the opposite: the trajectory is the bottleneck, not the student.”
“Each training trajectory is built through a chain of blind stochastic jumps with no evaluation of sequence quality; a single bad decision at an early midpoint propagates through subsequent steps”
Summary last validated Sep 17, 2026
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