Trajectory as the Teacher: Few-Step Discrete Flow Matching via Energy-Navigated Distillation
Apple Machine Learning Research
Discrete flow matching for text generation replaces noise tokens with language via many iterative forward passes, but distillation can train a student to follow a similar multi-step trajectory in far fewer steps. The approach targets the fact that the method can require hundreds of forward passes in the baseline process. It argues the trajectory quality—not the student’s capacity—is the bottleneck, since early low-quality decisions can propagate through later steps during training.
Why it matters
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. When the student underperforms, the usual explanation is insufficient capacity. 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…
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