Boosted GFlowNets: Improving Exploration via Sequential Learning

Pedro Dall'Antonia, Tiago Silva, Daniel Augusto de Souza, César Lincoln Mattos, Diego Mesquita

TL;DR: We propose a method to sequentially train and combine multiple GFlowNets, where each new model optimizes a residual reward function that accounts for reward captured so far by the existing ensemble, thereby focusing on missed modes.

Abstract:

Generative Flow Networks (GFlowNets) are powerful samplers for compositional objects that, by design, sample proportionally to a given non-negative reward. Nonetheless, in practice, they often struggle to explore the reward landscape evenly: trajectories toward easy-to-reach regions dominate training, while hard-to-reach modes receive vanishing or uninformative gradients, leading to poor coverage of high-reward areas.
We address this imbalance with Boosted GFlowNets, a method that sequentially trains an ensemble of GFlowNets, each optimizing a residual reward that compensates for the mass already captured by previous models.
This residual principle reactivates learning signals in underexplored regions and, under mild assumptions, ensures a monotone non-degradation property: adding boosters cannot worsen the learned distribution and typically improves it.
Empirically, Boosted GFlowNets achieve substantially better exploration and sample diversity on multimodal synthetic benchmarks and peptide design tasks, while preserving the stability and simplicity of standard trajectory-balance training.

Submission Number: 987