Gumbel Straight Flow: Distilling Autoregressive Models into One-step Flow Maps

Abstract:We present Gumbel Straight Flow (GSF), a continuous flow map language model that leverages the noise-data coupling of a pretrained autoregressive language (AR) model. We theoretically demonstrate that the coupling between Gumbel noise and one-hot token sequences induced by an autoregressive model yields non-intersecting linear paths connecting the noise to the sequence representations. To further enhance high-quality few-step path sampling, we use a flow map semigroup objective where the tangent (velocity) condition is guided directly by the AR teacher. Across various benchmarks, including pretraining and downstream tasks, GSF can outperform current few-step language generation baselines.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2610.00497 [cs.LG]

(or arXiv:2610.00497v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2610.00497 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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