让流模型在蛋白与分子设计中高效探索并精准优化。
Active Flow Matching
- 通过重构变分目标，实现梯度引导的流模型优化
- 前向KL版本在有限实验预算下表现优于主流基线
- 适合需要快速迭代的黑箱优化场景
离散扩散和流匹配模型通过并行迭代优化，捕捉高维目标空间中复杂的非加性和非自回归结构。然而其隐式生成特性难以与严谨的变分框架（如变分搜索分布VSD、自适应采样条件CbAS）结合，用于在线黑箱优化。本文提出主动流匹配（AFM），将变分目标重构为沿流路径的条件终点分布，实现梯度驱动的流模型定向优化，同时保持VSD与CbAS的理论严谨性。我们基于自归一化重要性采样推导了前向与反向KL变体。在一系列在线蛋白质与小分子设计任务中，前向KL AFM在有限实验预算下始终表现优异，展现出良好的探索-利用平衡能力。
原文摘要 · Abstract (English)
Discrete diffusion and flow matching models capture complex, non-additive and non-autoregressive structure in high-dimensional objective landscapes through parallel, iterative refinement. However, their implicit generative nature precludes direct integration with principled variational frameworks for online black-box optimisation, such as variational search distributions (VSD) and conditioning by adaptive sampling (CbAS). We introduce Active Flow Matching (AFM), which reformulates variational objectives to operate on conditional endpoint distributions along the flow, enabling gradient-based steering of flow models toward high-fitness regions while preserving the rigour of VSD and CbAS. We derive forward and reverse Kullback-Leibler (KL) variants using self-normalised importance sampling. Across a suite of online protein and small molecule design tasks, forward-KL AFM consistently performs competitively compared to state-of-the-art baselines, demonstrating effective exploration-exploitation under tight experimental budgets.
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