Information-Geometric Forward Policy Training in GFlowNets
The paper frames GFlowNet forward-policy updates as natural-gradient steps in the trajectory sampler’s Fisher-Rao geometry.
Raykov and Veiga derive a decomposition of the trajectory Fisher information into per-step conditional second moments. That separates cases where temporal score interactions disappear from cases where shared parameters leave dense couplings. The authors map this into exact, Monte Carlo, and structure-exploiting training regimes, with graphical-model methods proposed as surrogates when target structure can be used. They report empirical examples comparing Riemannian and Euclidean optimization on convergence and exploration behavior. ArXiv · AI/CL/LG's note
Raykov and Veiga derive a decomposition of the trajectory Fisher information into per-step conditional second moments. That separates cases where temporal score interactions disappear from cases where shared parameters leave dense couplings. The authors map this into exact, Monte Carlo, and structure-exploiting training regimes, with graphical-model methods proposed as surrogates when target structure can be used. They report empirical examples comparing Riemannian and Euclidean optimization on convergence and exploration behavior. ArXiv · AI/CL/LG's note
score 4