Offline multi-objective optimization (MOO) aims to recover Pareto-optimal designs given a finite, static dataset. Recent generative approaches, including diffusion models, show strong performance under hypervolume, yet their behavior under other established MOO metrics is less understood. We show that generative methods systematically underperform evolutionary alternatives with respect to other metrics, such as generational distance. We relate this failure mode to the offline-frontier shift, i.e., the displacement of the offline dataset from the Pareto front, which acts as a fundamental limitation in offline MOO. We argue that overcoming this limitation requires out-of-distribution sampling in objective space (via an integral probability metric) and empirically observe that generative methods remain conservatively close to the offline objective distribution. Our results position offline MOO as a distribution-shift--limited problem and provide a diagnostic lens for understanding when and why generative optimization methods fail.

翻译：离线多目标优化旨在通过有限静态数据集恢复帕累托最优设计方案。包括扩散模型在内的近期生成方法在超体积指标下表现优异，但其在其他成熟MOO度量标准下的行为尚未得到充分理解。本文证明生成式方法在代际距离等指标上系统性逊于进化算法。我们将这种失效模式归因于离线前沿偏移——即离线数据集与帕累托前沿之间的位移，这构成了离线MOO的根本性限制。我们认为突破此限制需要在目标空间进行分布外采样（通过积分概率度量），而实证观察表明生成式方法仍保守地贴近离线目标分布。本研究将离线MOO定位为分布偏移受限问题，并为理解生成式优化方法何时及为何失效提供了诊断视角。