In-Context Preference-Conditioned Pareto Set Learning from Limited Offline Data

The paper introduces In-Context Preference-Conditioned Pareto Set Learning (ICPSL), a framework that uses a pretrained hypernetwork to generate a problem-specific, preference-conditioned Pareto solution model from limited offline data. ICPSL eliminates the need for repeated preference-wise searches or problem-specific training by producing a reusable model in a single forward pass. Experiments on synthetic and real-world problems demonstrate that ICPSL yields high-quality Pareto solutions while significantly speeding up model construction.

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