Diffusion-based generative models increasingly rely on inference-time guidance, adding a drift term or reweighting mixture of experts, to improve sample quality on task-specific objectives. However, most existing techniques require repeated score or gradient evaluations, introducing bias, high computational overhead, or both. We introduce \texttt{URGE}, Unbiased Resampling via Girsanov Estimation, a derivative-free inference-time scaling algorithm that performs path-wise importance reweighting via a Girsanov change of measure. Instead of computing gradient-based particle weights in previous work, \texttt{URGE} attaches a simple multiplicative weight to each simulated trajectory and periodically resamples. No score, no Hessian, and no PDE evaluation is required. We establish an equivalence between path-wise and particle-wise SMC: the Girsanov path weight admits a backward conditional expectation that recovers the previous particle-level weights, guaranteeing that both schemes produce the same unbiased terminal law. Empirically, \texttt{URGE} outperforms existing inference-time guidance baselines on synthetic tests and diffusion-model benchmarks, achieving better generation quality, while being significantly simpler to implement and fully gradient-free.

翻译：基于扩散的生成模型日益依赖推理时引导，通过添加漂移项或对专家混合模型进行重加权，以提升特定任务目标下的样本质量。然而，现有大部分技术需要重复计算分数或梯度，导致引入偏差、产生高计算开销，或两者兼有。我们提出\texttt{URGE}（基于Girsanov估计的无偏重采样），一种无导数的推理时扩展算法，通过Girsanov测度变换实现路径级重要性重加权。与以往工作中计算基于梯度的粒子权重不同，\texttt{URGE}为每条模拟轨迹附加一个简单的乘性权重，并周期性地进行重采样。该方法无需计算分数、海森矩阵或偏微分方程。我们建立了路径级与粒子级序贯蒙特卡洛之间的等价性：Girsanov路径权重具有一个后向条件期望，可以恢复出先前的粒子级权重，从而保证两种方案产生相同的无偏终端分布。实验表明，在合成测试与扩散模型基准上，\texttt{URGE}优于现有推理时引导基线方法，在生成质量更优的同时，实现更为简单且完全无需梯度。