解决图生成中对称性导致的采样偏差问题，提升生成质量与多样性。
Symmetry-Aware GFlowNets
- 通过奖励缩放引入对称性修正，避免显式状态转移计算
- 实现无偏采样，生成高奖励图且分布更接近目标
- 适用于原子级与片段级图生成，适合分子设计等场景
生成流网络（GFlowNets）为按奖励比例采样图提供了强大框架。然而，现有方法因状态转移概率计算不准确，受图固有对称性影响，产生系统性偏差，无论基于原子还是片段的生成方案均受影响。为此，我们提出对称感知生成流网络（SA-GFN），通过奖励缩放将对称性修正融入学习过程。该方法将偏差校正直接嵌入奖励结构，无需显式状态转移计算。实验表明，SA-GFN可实现无偏采样，提升生成多样性，并持续生成接近目标分布的高奖励图。
原文摘要 · Abstract (English)
Generative Flow Networks (GFlowNets) offer a powerful framework for sampling graphs in proportion to their rewards. However, existing approaches suffer from systematic biases due to inaccuracies in state transition probability computations. These biases, rooted in the inherent symmetries of graphs, impact both atom-based and fragment-based generation schemes. To address this challenge, we introduce Symmetry-Aware GFlowNets (SA-GFN), a method that incorporates symmetry corrections into the learning process through reward scaling. By integrating bias correction directly into the reward structure, SA-GFN eliminates the need for explicit state transition computations. Empirical results show that SA-GFN enables unbiased sampling while enhancing diversity and consistently generating high-reward graphs that closely match the target distribution.
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