引导流图树采样实现在线反馈驱动搜索
BFMT: Enhancing Search Capabilities of Tree Sampler via Bootstrap Flow-Map Tree
- Department of CSE, Washington University in St.Louis, USA（美国圣路易斯华盛顿大学计算机科学与工程系）
- Department of Information Technology, Uppsala University, Sweden（瑞典乌普萨拉大学信息技术系）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对有限采样预算下的探索难题，介绍计算高效的引导流图树（BFMT）采样框架，可在预算约束下进行历史感知全局搜索与对齐，能高效分配预算，实验表明其性能远超基线方法。
AI中文摘要：
在许多科学和工程领域，在有限采样预算内最大化发现需要策略性、观察引导的探索。生成模型虽能实现无训练奖励对齐，但当前方法在局部搜索表现好，偏好未知时探索困难。为此引入BFMT，一种用于历史感知全局搜索和采样预算约束下对齐的高效采样框架，实验表明其性能远超基线方法。
英文摘要：
The ability to efficiently navigate high-dimensional spaces to identify optimal candidates-whether designing targeted molecular structures or generating images with preferred attributes-stands as a central pillar of modern scientific progress. Recent tree-based samplers enable highly scalable inference-time search by eliminating the reward-gradient bottleneck inherent to particle-based methods. Despite their potential, existing tree-based samplers are fundamentally constrained by the massive number of function evaluations (NFEs) required for node valuation, crippling their utility in exploration-heavy tasks. Furthermore, their reliance on small, uniform transitions at each depth precludes dynamic, adaptive search capabilities. To address this, we introduce Bootstrap Flow-Map-Tree (a.k.a BFMT), a computationally efficient sampling framework that enables full tree-path construction from any tree depth using a single function evaluation, drastically reducing computational overhead while providing critical foresight for sequential sampling. By enabling dynamic transition time-step scheduling, BFMT efficiently allocates its sampling budget, smoothly transitioning from broad global exploration to fine-grained local refinement of high-utility modes discovered through exploration. Extensive experiments and ablation studies across diverse domains-from high-dimensional images to large-scale molecules-demonstrate BFMT's superiority over baseline approaches in both search and alignment tasks.
