From Prediction to Ranking in Offline Model-Based Optimization

Offline model-based optimization (MBO) aims to discover high-performing designs using only a fixed dataset of past evaluations. Most existing methods learn a surrogate by pointwise regression, implicitly assuming that accurate value prediction is sufficient for reliable optimization. However, once the optimizer reaches a high-quality design, successful optimization depends on whether the surrogate ranks it above the remaining candidates, rather than on predicting their objective values accurately. To formalize this idea, we separate optimizer reachability from surrogate-based selection and define an optimization-oriented ranking risk between $\epsilon$-optimal and non-$\epsilon$-optimal candidates. We show that this ranking risk directly controls final selection error, whereas pointwise regression reaches the same optimization target only indirectly through regional prediction errors and the objective-margin distribution. The resulting bound further identifies the mismatch between training pairs and optimizer-induced target pairs as a key source of error, and relates this mismatch to the geometric separation of high-quality candidates from the offline data support. Guided by this analysis, we propose distribution-aware ranking (DAR), which concentrates training on high-quality-versus-remaining pairs to better match the optimization-relevant comparison distribution. Experiments on five Design-Bench tasks validate the predicted extrapolation difficulty and show that DAR achieves the best average rank among the evaluated methods.