A new arXiv preprint, 'Towards Understanding the Training of GFlowNets: A Theoretical Perspective,' takes a formal look at how Generative Flow Networks (GFlowNets) are trained. GFlowNets are policies designed to sample discrete objects in proportion to a given positive reward, a property that makes them useful for tasks like combinatorial exploration and probabilistic inference.
The paper explains that training proceeds by minimizing balance objectives. When these objectives are satisfied exactly, the resulting policy is guaranteed to sample correctly from the target distribution. This establishes a clear theoretical link between the training loss and the desired sampling behavior.
However, the abstract's final sentence begins with 'However,' and the excerpt ends there. The full paper presumably discusses limitations, open questions, or conditions under which the guarantee may not hold, but those details are not available in the provided source. Readers should consult the full arXiv listing for the complete argument.