Inference-Time Alignment for Diffusion Models via Doob's Matching

• URL: http://arxiv.org/abs/2601.06514v1
• Date: Sat, 10 Jan 2026 10:28:06 GMT
• Title: Inference-Time Alignment for Diffusion Models via Doob's Matching
• Authors: Jinyuan Chang, Chenguang Duan, Yuling Jiao, Yi Xu, Jerry Zhijian Yang,
• Abstract summary: Inference-time alignment for diffusion models aims to adapt a pre-trained diffusion model toward a target distribution without retraining the base score network.<n>We introduce Doob's matching, a novel framework for guidance estimation grounded in Doob's $h$-transform.<n>We prove non-asymptotic convergence guarantees for the generated distributions in the 2-Wasserstein distance.
• Score: 16.416975860645724
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Inference-time alignment for diffusion models aims to adapt a pre-trained diffusion model toward a target distribution without retraining the base score network, thereby preserving the generative capacity of the base model while enforcing desired properties at the inference time. A central mechanism for achieving such alignment is guidance, which modifies the sampling dynamics through an additional drift term. In this work, we introduce Doob's matching, a novel framework for guidance estimation grounded in Doob's $h$-transform. Our approach formulates guidance as the gradient of logarithm of an underlying Doob's $h$-function and employs gradient-penalized regression to simultaneously estimate both the $h$-function and its gradient, resulting in a consistent estimator of the guidance. Theoretically, we establish non-asymptotic convergence rates for the estimated guidance. Moreover, we analyze the resulting controllable diffusion processes and prove non-asymptotic convergence guarantees for the generated distributions in the 2-Wasserstein distance.

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