Phylogenetics is a branch of computational biology that studies the
evolutionary relationships among biological entities. Its long history and
numerous applications notwithstanding, inference of phylogenetic trees from
sequence data remains challenging: the high complexity of tree space poses a
significant obstacle for the current combinatorial and probabilistic
techniques. In this paper, we adopt the framework of generative flow networks
(GFlowNets) to tackle two core problems in phylogenetics: parsimony-based and
Bayesian phylogenetic inference. Because GFlowNets are well-suited for sampling
complex combinatorial structures, they are a natural choice for exploring and
sampling from the multimodal posterior distribution over tree topologies and
evolutionary distances. We demonstrate that our amortized posterior sampler,
PhyloGFN, produces diverse and high-quality evolutionary hypotheses on real
benchmark datasets. PhyloGFN is competitive with prior works in marginal
likelihood estimation and achieves a closer fit to the target distribution than
state-of-the-art variational inference methods