AI RESEARCH
CoAction: Cross-task Correlation-aware Pareto Set Learning
arXiv CS.LG
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ArXi:2605.01712v1 Announce Type: new Pareto set learning (PSL) is an emerging paradigm in multi-objective optimization that trains neural networks to map preference vectors to Pareto optimal solutions. However, existing PSL methods primarily focus on solving a single multi-objective optimization problem at a time. This limitation not only increases computational costs in multi-objective multitask optimization scenarios by requiring a separate model for each task, but also fails to exploit the inter-task correlations across tasks.