An Open-Source Training Dataset for Foundation Models for Black-box Optimization

ArXi:2605.23417v1 Announce Type: new Most black-box optimization methods require extensive hyperparameter tuning, often limiting their ability to generalize across different optimization domains. Foundation models for black-box optimization that learn optimization principles from a large collection of optimization trajectories offer a promising alternative, with the potential to outperform manually designed methods across diverse problem classes.