About "Flow Network based Generative Models for Non-Iterative Diverse Candidate Generation"？ I am reading the Yoshua Bengio et al, Flow Network based Generative Models for Non-Iterative Diverse Candidate Generation. After read this paper, I wonder how to establish a GFlowNet if I don't know all the States?
That is, I only have goals, such as maximum likelihood estimation or some generation models similar to MCMC. How can I initialize the GFlowNet nodes when I don't know all the states in advance?

As in deep reinforcement learning, one does not need to literally hold the state space in memory explicitly (i.e. we don't need to "know all the states"). Rather, what is used is a function approximator that maps a description of a state to a prediction. In DRL that would be for example $Q_\theta(s,a)$, or $\pi_\theta(a|s)$.
In this paper, the flow $F_\theta(s,a)$ is what is being approximated, with a deep neural network. All that is needed is thus a description of the current state $s$ that can be given to a neural network (e.g. an image). In the paper, a graph is used as an input to a graph neural network. These graphs are not to be confused with the state space DAG, which in this work is a graph of (molecular) graphs, where an edge represents a graph edit.
Disclaimer, I am the first author of this paper.
