Range-Calibrated GFlowNets

Generative Flow Networks (GFlowNets) learn policies for sampling compositional objects in proportion to a specified terminal reward. Applying an inverse temperature to reward values controls concentration, but the same temperature can assign different probabilities to a top quantile on different reward landscapes. To specify target concentration, we propose Range-Calibrated GFlowNets (RC-GFN), which use exponential weights on ranks estimated against a policy-independent reference set. Under a uniform reference measure without score ties, the concentration parameter determines the population target's top-quantile mass in closed form, independently of terminal-space size and reward values. The empirical weights are invariant to strictly increasing score transformations and have a user-controlled log-range bound. For arbitrary reference measures and tied scores, we bound finite-reference target error independently of terminal-space size and separate it from policy-fitting error. Experiments on an enumerable environment examine concentration across landscapes and reference sizes. On molecular and biological sequence design tasks, RC-GFN is competitive with the tested temperature-conditional GFlowNets in both mode discovery and top-candidate reward, and outperforms a representative rank-based GFlowNet.