提出精确离散引导方法，采样效率显著提升。
Discrete Guidance Matching: Exact Guidance for Discrete Flow Matching
- 基于离散流模型推导出精确转移率，实现单步前向引导
- 在文本到图像生成等任务中显著提升偏好对齐效果
- 通用框架可兼容现有方法，适合高效生成场景
引导通过引导生成过程向目标分布收敛，提供简单有效的后验采样框架。针对离散数据建模，现有方法多采用一阶近似引导以提升采样效率，但在离散状态空间中该近似误差较大。本文提出一种新引导框架：基于已学习的离散流匹配模型，推导出目标分布的精确转移率，使引导仅需每步一次前向传播，显著提高效率。该统一框架具有普适性，包含现有引导方法作为特例，并可无缝应用于掩码扩散模型。我们在能量引导模拟及文本到图像生成、多模态理解任务中的偏好对齐上验证了该方法的有效性。代码已公开于 https://github.com/WanZhengyan/Discrete-Guidance-Matching。
原文摘要 · Abstract (English)
Guidance provides a simple and effective framework for posterior sampling by steering the generation process towards the desired distribution. When modeling discrete data, existing approaches mostly focus on guidance with the first-order approximation to improve the sampling efficiency. However, such an approximation is inappropriate in discrete state spaces since the approximation error could be large. A novel guidance framework for discrete data is proposed to address this problem: we derive the exact transition rate for the desired distribution given a learned discrete flow matching model, leading to guidance that only requires a single forward pass in each sampling step, significantly improving efficiency. This unified novel framework is general enough, encompassing existing guidance methods as special cases, and it can also be seamlessly applied to the masked diffusion model. We demonstrate the effectiveness of our proposed guidance on energy-guided simulations and preference alignment on text-to-image generation and multimodal understanding tasks. The code is available at https://github.com/WanZhengyan/Discrete-Guidance-Matching.
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