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苹果研究提出新方法优化离散流匹配文本生成效率

Trajectory as the Teacher: Few-Step Discrete Flow Matching via Energy-Navigated Distillation

精选理由

苹果搞了个新方法,能让文本生成更快,比之前的方法少走很多弯路。

苹果提出一种新方法通过能量引导蒸馏技术,将原本需要数百步的离散流匹配文本生成过程缩短到少数几步,解决了传统方法中因早期随机跳跃导致序列质量下降的问题。

图片来源 · Apple ML Research
原文 · Apple ML Research

Trajectory as the Teacher: Few-Step Discrete Flow Matching via Energy-Navigated Distillation

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…