Order-Preserving GFlowNets

arXiv:2310.00386v2 Announce Type: replace
Abstract: Generative Flow Networks (GFlowNets) have been introduced as a method to sample a diverse set of candidates with probabilities proportional to a given reward. However, GFlowNets can only be used with a predefined scalar reward, which can be either computationally expensive or not directly accessible, in the case of multi-objective optimization (MOO) tasks for example. Moreover, to prioritize identifying high-reward candidates, the conventional practice is to raise the reward to a higher exponent, the optimal …

abstract arxiv case cs.ai cs.lg diverse flow generative multi-objective networks optimization sample set stat.ml tasks type