Multi-objective optimization (MOO) arises in many real-world applications where trade-offs between competing objectives must be carefully balanced. In the offline setting, where only a static dataset is available, the main challenge is generalizing beyond observed data. We introduce Pareto-Conditioned Diffusion (PCD), a novel framework that formulates offline MOO as a conditional sampling problem. By conditioning directly on desired trade-offs, PCD avoids the need for explicit surrogate models. To effectively explore the Pareto front, PCD employs a reweighting strategy that focuses on high-performing samples and a reference-direction mechanism to guide sampling towards novel, promising regions beyond the training data. Experiments on standard offline MOO benchmarks show that PCD achieves highly competitive performance and, importantly, demonstrates greater consistency across diverse tasks than existing offline MOO approaches.

翻译：多目标优化（MOO）广泛存在于现实应用中，其中相互竞争的目标之间必须仔细权衡。在仅能获取静态数据集的离线设置中，主要挑战在于对观测数据之外进行泛化。本文提出帕累托条件扩散（PCD），一种将离线MOO构建为条件采样问题的新框架。通过直接以期望的权衡为条件，PCD避免了显式代理模型的需求。为有效探索帕累托前沿，PCD采用重加权策略以聚焦高性能样本，并引入参考方向机制引导采样朝向训练数据之外新颖且有潜力的区域。在标准离线MOO基准上的实验表明，PCD实现了极具竞争力的性能，且相较于现有离线MOO方法，在不同任务间展现出更高的一致性。