How to Guide Your Language Flow

2026-09-16

AI Summary

本文介绍了一种名为探针引导的新方法，利用现有扩散模型的冻结内部状态来构建指导信号，以改进连续扩散语言模型的无条件生成和多项选择题回答性能。

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.

Problem

• flow matching models
• probe guidance
• diffusion language models
• unconditional generation
• question answering

Innovation

• probe guidance
• diffusion models
• autoguidance
• language flow