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arXiv · 2610.00497

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

Yeongmin Kim·Arnaud Doucet·Andrew Campbell·Valentin De Bortoli·Thomas Mensink·David Ruhe
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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.

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Yeongmin Kim, Arnaud Doucet, Andrew Campbell, Valentin De Bortoli, Thomas Mensink, David Ruhe. 2026-09-30. Gumbel Straight Flow: Distilling Autoregressive Models into One-step Flow Maps. https://arxiv.org/abs/2610.00497

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