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（GFlowNets或GFNs）是基于马尔可夫流的概率模型，采用特定的摊销算法学习随机策略，以生成包括生物分子、化学材料等在内的组合物质。凭借生成高性能生化分子的强大能力，GFNs加速了科学物质的发现，有效克服了传统材料发现方法耗时、费力且成本高昂的缺陷。然而，以往研究鲜少关注通过调整生成结构来积累探索经验，这导致在复杂采样空间中方向迷失。针对该问题的现有努力（如LS-GFN）仅限于局部贪婪搜索，缺乏更广泛的全局调整。本文引入了一种新颖的GFN变体——动态回溯GFN（DB-GFN），通过基于奖励的动态回溯机制改进了决策步骤的自适应能力。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，从而纠正不利决策并在探索过程中探索替代路径。当应用于涉及生化分子和遗传物质序列的生成任务时，DB-GFN在样本质量、样本探索数量以及训练收敛速度方面均优于LS-GFN和GTB等GFN模型以及传统强化学习方法。此外，由于DB-GFN具有正交特性，它在未来改进GFNs方面展现出巨大潜力，并可与其他策略结合以实现更高的搜索性能。