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.

本篇论文提出了一种新的 GFlowNet 变体，遗传引导 GFlowNet (Genetic GFN)，通过将迭代遗传搜索集成到 GFlowNet 中，有效地指导 GFlowNet 到高回报区域，解决全局过度探索导致训练效率低下和探索有限区域的问题。此方法在实际分子优化 (PMO) 的基准测试中达到了 16.213 的最高分数，明显优于报道的最佳分数 15.185，超过了 23 个任务中的所有基准线，包括强化学习、贝叶斯优化、生成模型、GFlowNets 和遗传算法。