Generative Flow Networks (GFlowNets or GFNs) are probabilistic models predicated on Markov flows, and they employ specific amortization algorithms to learn stochastic policies that generate compositional substances including biomolecules, chemical materials, etc. With a strong ability to generate high-performance biochemical molecules, GFNs accelerate the discovery of scientific substances, effectively overcoming the time-consuming, labor-intensive, and costly shortcomings of conventional material discovery methods. However, previous studies rarely focus on accumulating exploratory experience by adjusting generative structures, which leads to disorientation in complex sampling spaces. Efforts to address this issue, such as LS-GFN, are limited to local greedy searches and lack broader global adjustments. This paper introduces a novel variant of GFNs, the Dynamic Backtracking GFN (DB-GFN), which improves the adaptability of decision-making steps through a reward-based dynamic backtracking mechanism. DB-GFN allows backtracking during the network construction process according to the current state's reward value, thereby correcting disadvantageous decisions and exploring alternative pathways during the exploration process. When applied to generative tasks involving biochemical molecules and genetic material sequences, DB-GFN outperforms GFN models such as LS-GFN and GTB, as well as traditional reinforcement learning methods, in sample quality, sample exploration quantity, and training convergence speed. Additionally, owing to its orthogonal nature, DB-GFN shows great potential in future improvements of GFNs, and it can be integrated with other strategies to achieve higher search performance.

翻译：生成流网络（Generative Flow Networks, GFlowNets或GFNs）是基于马尔可夫流的概率模型，通过特定摊销算法学习随机策略，用于生成生物分子、化学材料等组合物质。凭借生成高性能生化分子的强大能力，GFlowNets加速了科学物质的发现，有效克服了传统物质发现方法耗时、费力且成本高昂的缺陷。然而，先前研究鲜少通过调整生成结构来积累探索经验，导致其在复杂采样空间中缺乏方向性。针对此问题的改进方法（如LS-GFN）局限于局部贪婪搜索，缺乏更广泛的全局调整。本文提出GFlowNets的新型变体——动态回溯GFN（DB-GFN），通过基于奖励的动态回溯机制提升决策步骤的自适应性。DB-GFN允许在网络构建过程中根据当前状态的奖励值进行回溯，从而在探索过程中修正不利决策并探索替代路径。在涉及生化分子及基因物质序列的生成任务中，DB-GFN在样本质量、样本探索数量及训练收敛速度方面均优于LS-GFN、GTB等GFN模型及传统强化学习方法。此外，由于DB-GFN的正交特性，它在未来GFlowNets改进中展现出巨大潜力，可结合其他策略实现更优的搜索性能。