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Generative flow networks (GFlowNets) are a family of algorithms for traininga sequential sampler of discrete objects under an unnormalized target densityand have been successfully used for various probabilistic modeling tasks.Existing training objectives for GFlowNets are either local to states ortransitions, or propagate a reward signal over an entire sampling trajectory.We argue that these alternatives represent opposite ends of a gradientbias-variance tradeoff and propose a way to exploit this tradeoff to mitigateits harmful effects. Inspired by the TD($\lambda$) algorithm in reinforcementlearning, we introduce subtrajectory balance or SubTB($\lambda$), a GFlowNettraining objective that can learn from partial action subsequences of varyinglengths. We show that SubTB($\lambda$) accelerates sampler convergence inpreviously studied and new environments and enables training GFlowNets inenvironments with longer action sequences and sparser reward landscapes thanwhat was possible before. We also perform a comparative analysis of stochasticgradient dynamics, shedding light on the bias-variance tradeoff in GFlowNettraining and the advantages of subtrajectory balance.

Learning GFlowNets from partial episodes for improved convergence and stability