Generative Flow Networks (GFlowNets) have emerged as a powerful paradigm for generating composite structures, demonstrating considerable promise across diverse applications. While substantial progress has been made in exploring their modeling validity and connections to other generative frameworks, the theoretical understanding of their learning behavior remains largely uncharted. In this work, we present a rigorous theoretical investigation of GFlowNets' learning behavior, focusing on four fundamental dimensions: convergence, sample complexity, implicit regularization, and robustness. By analyzing these aspects, we seek to elucidate the intricate mechanisms underlying GFlowNet's learning dynamics, shedding light on its strengths and limitations. Our findings contribute to a deeper understanding of the factors influencing GFlowNet performance and provide insights into principled guidelines for their effective design and deployment. This study not only bridges a critical gap in the theoretical landscape of GFlowNets but also lays the foundation for their evolution as a reliable and interpretable framework for generative modeling. Through this, we aspire to advance the theoretical frontiers of GFlowNets and catalyze their broader adoption in the AI community.

翻译：生成流网络已成为生成复合结构的有力范式，在多种应用中展现出巨大潜力。尽管在探索其建模有效性及与其他生成框架的联系方面已取得实质性进展，但其学习行为的理论理解在很大程度上仍属未知领域。本文对生成流网络的学习行为进行了严格的理论研究，聚焦于四个基本维度：收敛性、样本复杂度、隐式正则化与鲁棒性。通过分析这些方面，我们试图阐明生成流网络学习动态背后的复杂机制，揭示其优势与局限。我们的研究结果有助于更深入地理解影响生成流网络性能的因素，并为其实效性设计与部署提供原则性指导。本研究不仅填补了生成流网络理论图景中的关键空白，也为其发展成为一个可靠且可解释的生成建模框架奠定了基础。藉此，我们期望推动生成流网络的理论前沿，并促进其在人工智能社区中的更广泛采用。