  This paper studies generative flow networks (GFlowNets) to sample objects
from the Boltzmann energy distribution via a sequence of actions. In
particular, we focus on improving GFlowNet with partial inference: training
flow functions with the evaluation of the intermediate states or transitions.
To this end, the recently developed forward-looking GFlowNet reparameterizes
the flow functions based on evaluating the energy of intermediate states.
However, such an evaluation of intermediate energies may (i) be too expensive
or impossible to evaluate and (ii) even provide misleading training signals
under large energy fluctuations along the sequence of actions. To resolve this
issue, we propose learning energy decompositions for GFlowNets (LED-GFN). Our
main idea is to (i) decompose the energy of an object into learnable potential
functions defined on state transitions and (ii) reparameterize the flow
functions using the potential functions. In particular, to produce informative
local credits, we propose to regularize the potential to change smoothly over
the sequence of actions. It is also noteworthy that training GFlowNet with our
learned potential can preserve the optimal policy. We empirically verify the
superiority of LED-GFN in five problems including the generation of
unstructured and maximum independent sets, molecular graphs, and RNA sequences.
