这篇论文提出了IMPFM，一个用多粒子流映射做在线反馈搜索的方法，能全局探索还不容易过拟合。跟标准SMC比，它解决了权重退化问题，效果更好。
论文提出IMPFM框架，通过多粒子流映射实现样本高效的在线反馈驱动搜索。该框架引入基于流映射的后验样本共享机制，在每次重采样步骤中用全体粒子的集体后验样本纠正个体漂移，从而最大化样本效用并实现全局探索。同时，采用多粒子交互的探索-利用重加权机制，维持结构多样性并克服标准SMC采样器的权重退化。理论证明该框架产生多粒子交互感知的Feynman-Kac修正器，逐步将多粒子系统引导至KL倾斜目标分布，防止模式坍缩。在多种搜索和对齐任务上的实验表明IMPFM优于现有基线。
Sequentially-Controlled Interactive Multi-Particle Flow-Maps for Online Feedback-Driven Search
While generative models have enabled training-free reward alignment, current methods typically excel in local exploration within narrow regions of the underlying distribution. These approaches struggle when preferences are unknown a priori and only revealed through sequential feedback-a scenario demanding broad exploration to uncover high-utility regions. To address this, we propose Sequentially-Controlled Interactive Multi-Particle Flow-Maps (IMPFM), a framework for sample-efficient online feedback-driven search. IMPFM progressively transports a group of interactive particles toward the target distribution, maintaining the broad coverage essential for heterogeneous preference alignment. IMPFM introduces a principled and efficient posterior sample sharing mechanism across particles powered by flow maps. By correcting individual particle drift with the collective posterior samples of the entire ensemble at each resampling step, the framework maximizes sample utility to enable global exploration while actively mitigating reward over-optimization, typical of standard control frameworks. Paired with a principled exploration-exploitation reweighting mechanism involving multi-particle interaction, this sequentially corrected multi-particle dynamics explicitly preserves structural diversity and overcomes the weight degeneracy inherent to standard SMC samplers. Crucially, we prove that the resulting sampling framework yields a multi-particle interaction-aware Feynman-Kac corrector that progressively steers the multi-particle system toward a KL-tilted target distribution, facilitating global exploration and preventing mode collapse. Extensive empirical evaluations and rigorous ablations across diverse search and alignment tasks confirm the efficacy of IMPFM over existing baselines.