Towards Understanding the Training of GFlowNets: A Theoretical Perspective
The authors develop a unified theory linking balance loss, approximate stationarity, and sampling accuracy for GFlowNets. They show that controlling balance loss bounds terminal distribution error by analyzing how well approximate stationarity controls parameterization incompleteness. This establishes finite-time convergence under specific training conditions.