This paper proposes a novel variant of GFlowNet, genetic-guided GFlowNet (Genetic GFN), which integrates an iterative genetic search into GFlowNet. Genetic search effectively guides the GFlowNet to high-rewarded regions, addressing global over-exploration that results in training inefficiency and exploring limited regions. In addition, training strategies, such as rank-based replay training and unsupervised maximum likelihood pre-training, are further introduced to improve the sample efficiency of Genetic GFN. The proposed method shows a state-of-the-art score of 16.213, significantly outperforming the reported best score in the benchmark of 15.185, in practical molecular optimization (PMO), which is an official benchmark for sample-efficient molecular optimization. Remarkably, ours exceeds all baselines, including reinforcement learning, Bayesian optimization, generative models, GFlowNets, and genetic algorithms, in 14 out of 23 tasks.

翻译：本文提出了一种GFlowNet的新变体——遗传引导的GFlowNet（Genetic GFN），它将迭代式遗传搜索整合到GFlowNet中。遗传搜索有效引导GFlowNet进入高奖励区域，解决了因全局过度探索导致的训练效率低下和探索区域受限的问题。此外，进一步引入了基于排名的重放训练和无监督最大似然预训练等训练策略，以提高Genetic GFN的样本效率。所提出的方法在实际分子优化（PMO）中取得了16.213的先进分数，显著超过了该基准中报告的15.185最佳分数。PMO是一个用于样本高效分子优化的官方基准。值得注意的是，在23个任务中，我们的方法在14个任务上超越了所有基线方法，包括强化学习、贝叶斯优化、生成模型、GFlowNet和遗传算法。