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.
@article{arxiv.2510.00805,
title = {Planning-Augmented Sampling with Early Guidance for High-Reward Discovery},
author = {Rui Zhu and Yudong Zhang and Xuan Yu and Chen Zhang and Xu Wang and Yang Wang},
journal= {arXiv preprint arXiv:2510.00805},
year = {2026}
}