用规划增强采样，让生成模型更快找到高回报解。
Planning-Augmented Sampling with Early Guidance for High-Reward Discovery
- 结合蒙特卡洛树搜索与软贪心机制，动态引导生成路径。
- 在分子设计等任务中，早期发现高奖励样本速度提升显著。
- 适合需要快速优质解的生成任务，如药物分子设计。
生成流网络（GFlowNets）具备内在多样性，但现有采样策略依赖弱引导探索，导致高回报候选物发现缓慢。在分子设计等任务中，快速且一致地生成高质量解比精确匹配分布更重要。本文提出一种规划增强框架：利用多项式上置信界进行蒙特卡洛树搜索，实时估计价值，并通过可控的软贪心机制将规划信号融入GFlowNets前向策略。该设计促进早期对高回报路径的探索，并随经验积累逐步转向策略驱动的利用。实验表明，该方法显著加速了早期高回报发现，持续生成高质量样本，并在代表性任务中保持多样性。所有代码已开源：https://github.com/ZRNB/PLUS。
原文摘要 · Abstract (English)
Generative Flow Networks (GFlowNets) enable structured generation with inherent diversity, but existing sampling strategies often rely on weak guided exploration, slowing early discovery of high-reward candidates. In tasks such as molecular design, rapid and consistent generation of high-reward solutions can outweigh faithful distribution matching. We propose a planning-augmented framework in which Monte Carlo Tree Search using polynomial upper confidence bounds provides online value estimates, and a controllable soft-greedy mechanism integrates these planning signals into the GFlowNets forward policy. This design fosters early exploration of high-reward trajectories and gradually shifts to policy-driven exploitation as experience accumulates. Empirical results show that our method accelerates early high-reward discovery, sustains top-quality sample generation, and preserves diversity across representative tasks. All implementations are available at https://github.com/ZRNB/PLUS.
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