We present GFlowState, a visual analytics system designed to illuminate the training process of Generative Flow Networks (GFlowNets or GFNs). GFlowNets are a probabilistic framework for generating samples proportionally to a reward function. While GFlowNets have proved to be powerful tools in applications such as molecule and material discovery, their training dynamics remain difficult to interpret. Standard machine learning tools allow metric tracking but do not reveal how models explore the sample space, construct sample trajectories, or shift sampling probabilities during training. Our solution, GFlowState, allows users to analyze sampling trajectories, compare the sample space relative to reference datasets, and analyze the training dynamics. To this end, we introduce multiple views, including a chart of candidate rankings, a state projection, a node-link diagram of the trajectory network, and a transition heatmap. These visualizations enable GFlowNet developers and users to investigate sampling behavior and policy evolution, and to identify underexplored regions and sources of training failure. Case studies demonstrate how the system supports debugging and assessing the quality of GFlowNets across application domains. By making the structural dynamics of GFlowNets observable, our work enhances their interpretability and can accelerate GFlowNet development in practice.

翻译：我们提出了GFlowState，一个旨在揭示生成流网络（GFlowNets或GFNs）训练过程的可视分析系统。GFlowNets是一种概率框架，用于按奖励函数成比例地生成样本。尽管GFlowNets在分子与材料发现等应用中已被证明是强有力的工具，但其训练动态仍然难以解释。标准机器学习工具允许指标跟踪，但未能揭示模型如何探索样本空间、构建样本轨迹或在训练过程中转移采样概率。我们的解决方案GFlowState使用户能够分析采样轨迹、比较样本空间与参考数据集的关系，并分析训练动态。为此，我们引入了多个视图，包括候选排名图、状态投影图、轨迹网络的节点-链接图以及转移热力图。这些可视化使GFlowNet开发者和用户能够研究采样行为与策略演化，并识别未被充分探索的区域及训练失败的根源。案例研究展示了该系统如何支持跨应用领域的GFlowNet调试与质量评估。通过使GFlowNet的结构动态变得可观测，我们的工作增强了其可解释性，并可加速GFlowNet在实践中的开发。