Simple Approximation and Derivative Free Inference-Time Scaling for Diffusion Models via Sequential Monte Carlo on Path Measures

Modern generative models have emerged as a powerful Diffusion-based generative models increasingly paradigm for learning complex, high-dimensional data distributions. In particular, diffusion models (Ho et al., 2020; rely on inference-time guidance, adding a drift Sohl-Dickstein et al., 2015; Song and Ermon, 2019; Song term or reweighting mixture of experts, to imet al., 2020) and flow-based methods (Zhang et al., 2018a; prove sample quality on task-specific objectives. However, most existing techniques reLipman et al., 2022; Albergo and Vanden-Eijnden, 2022; Liu quire repeated score or gradient evaluations, inet al., 2022) provide a principled and scalable framework for generative modeling, achieving state-of-the-art performance troducing bias, high computational overhead, or across diverse applications, including video generation (Ho both. We introduce URGE, approximation-free et al., 2022), protein design (Gruver et al., 2023), and largeResampling via Girsanov Estimation, a derivativefree inference-time scaling algorithm that perscale text generation (Li et al., 2022; Nie et al., 2025). A forms pathwise importance reweighting via a Girunifying perspective underlying these approaches is their formulation in terms of stochastic differential equations sanov change of measure.