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arXiv · 2506.18165

Non-equilibrium Annealed Adjoint Sampler

Jaemoo Choi·Yongxin Chen·Molei Tao·Guan-Horng Liu
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Abstract

Recently, there has been significant progress in learning-based diffusion samplers, which aim to sample from a given unnormalized density. Many of these approaches formulate the sampling task as a stochastic optimal control (SOC) problem using a canonical uninformative reference process, which limits their ability to efficiently guide trajectories toward the target distribution. In this work, we propose the Non-Equilibrium Annealed Adjoint Sampler (NAAS), a novel SOC-based diffusion framework that employs annealed reference dynamics as a non-stationary base SDE. This annealing structure provides a natural progression toward the target distribution and generates informative reference trajectories, thereby enhancing the stability and efficiency of learning the control. Owing to our SOC formulation, our framework can incorporate a variety of SOC solvers, thereby offering high flexibility in algorithmic design. As one instantiation, we employ a lean adjoint system inspired by adjoint matching, enabling efficient and scalable training. We demonstrate the effectiveness of NAAS across a range of tasks, including sampling from classical energy landscapes and molecular Boltzmann distributions.

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Jaemoo Choi, Yongxin Chen, Molei Tao, Guan-Horng Liu. 2025-11-25. Non-equilibrium Annealed Adjoint Sampler. https://arxiv.org/abs/2506.18165

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