Generative Flow Networks (GFlowNets) are amortized sampling methods that learn a distribution over discrete objects proportional to their rewards. GFlowNets exhibit a remarkable ability to generate diverse samples, yet occasionally struggle to consistently produce samples with high rewards due to over-exploration on wide sample space. This paper proposes to train GFlowNets with local search, which focuses on exploiting high-rewarded sample space to resolve this issue. Our main idea is to explore the local neighborhood via backtracking and reconstruction guided by backward and forward policies, respectively. This allows biasing the samples toward high-reward solutions, which is not possible for a typical GFlowNet solution generation scheme, which uses the forward policy to generate the solution from scratch. Extensive experiments demonstrate a remarkable performance improvement in several biochemical tasks. Source code is available: \url{https://github.com/dbsxodud-11/ls_gfn}.

翻译：生成流网络（GFlowNets）是一种平摊化采样方法，可学习与离散对象奖励成正比的数据分布。GFlowNets具有生成多样样本的显著能力，但偶尔因在宽采样空间中的过度探索而难以持续生成高奖励样本。本文提出通过局部搜索训练GFlowNets，聚焦于利用高奖励样本空间来解决该问题。我们的核心思想是通过反向策略和前向策略分别引导的回溯与重构探索局部邻域，从而将样本偏向高奖励解——这在典型的GFlowNet解决方案生成机制（仅使用前向策略从头生成解）中无法实现。大量实验表明，该方法在多项生化任务中展现出显著的性能提升。源代码详见：\url{https://github.com/dbsxodud-11/ls_gfn}。