[Submitted on 16 Sep 2026]
Title:How to Guide Your Language Flow
View a PDF of the paper titled How to Guide Your Language Flow, by Rohit Dilip and 5 other authors
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Abstract:We introduce a new method to guide flow matching models. Our approach, which we call probe guidance, uses the frozen internal states of an existing diffusion model to construct a guidance signal. This works using a similar principle as autoguidance, but eliminates the need for an additional forward pass at inference time and provides a reliable path to ensure that the weak and strong model share similar dynamics. We apply and benchmark this method on continuous diffusion language models, where probe guidance sets a new state-of-the-art performance on unconditional generation. When applied to a 1.7B diffusion language model, probe guidance consistently improves on multiple choice question answering benchmarks. Using our probes, we study the traditional autoguidance setting where the strong model is a weak checkpoint, and find that the weak model must come from a low-entropy region of training. These findings both provide a practical way to improve diffusion language models and shed light on the actual mechanism behind autoguidance, which is currently poorly understood.
Subjects:
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
Cite as: arXiv:2609.19356 [cs.LG]
(or arXiv:2609.19356v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2609.19356
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Rohit Dilip [view email] [v1] Wed, 16 Sep 2026 19:29:07 UTC (14,711 KB)
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