Boosted GFlowNets: Improving Exploration via Sequential Learning
This addresses exploration issues in GFlowNets for generative modeling, offering an incremental improvement with practical benefits in domains like peptide design.
The paper tackled the problem of GFlowNets struggling with uneven exploration in reward landscapes, resulting in poor coverage of high-reward areas, and introduced Boosted GFlowNets, which sequentially train an ensemble to improve exploration, achieving substantially better sample diversity on multimodal synthetic benchmarks and peptide design tasks.
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.