  Generative Flow Networks (GFlowNets) treat sampling from distributions over
compositional discrete spaces as a sequential decision-making problem, training
a stochastic policy to construct objects step by step. Recent studies have
revealed strong connections between GFlowNets and entropy-regularized
reinforcement learning. Building on these insights, we propose to enhance
planning capabilities of GFlowNets by applying Monte Carlo Tree Search (MCTS).
Specifically, we show how the MENTS algorithm (Xiao et al., 2019) can be
adapted for GFlowNets and used during both training and inference. Our
experiments demonstrate that this approach improves the sample efficiency of
GFlowNet training and the generation fidelity of pre-trained GFlowNet models.
