Generative flow networks (GFlowNets) are a family of algorithms that learn a
generative policy to sample discrete objects $x$ with non-negative reward
$R(x)$. Learning objectives guarantee the GFlowNet samples $x$ from the target
distribution $p^*(x) \propto R(x)$ when loss is globally minimized over all
states or trajectories, but it is unclear how well they perform with practical
limits on training resources. We introduce an efficient evaluation strategy to
compare the learned sampling distribution to the target reward distribution. As
flows can be underdetermined given training data, we clarify the importance of
learned flows to generalization and matching $p^*(x)$ in practice. We
investigate how to learn better flows, and propose (i) prioritized replay
training of high-reward $x$, (ii) relative edge flow policy parametrization,
and (iii) a novel guided trajectory balance objective, and show how it can
solve a substructure credit assignment problem. We substantially improve sample
efficiency on biochemical design tasks.

本文介绍了基于 GFlowNets 算法的生成模型策略，探究了如何在实际训练资源限制下实现更好的样本效率和匹配目标分布，提出了优先回放、相对边流策略参数化和新的引导轨迹平衡目标等方法来提高样本效率，有效解决了一些结构学分配问题。