Principled Data Generation for MetaBBO via Active Task Selection

Meta-Black-Box Optimization (MetaBBO) aims to acquire generalized optimization strategies by training on extensive sets of functions. While traditional approaches predominantly rely on fixed benchmarks, recent efforts have shifted towards maximizing landscape diversity to broaden training distribution coverage. However, we identify a critical Diversity-Quality Gap: Simply increasing landscape variety often yields tasks that are misaligned with the agent's evolving capabilities. To address this, we propose Hierarchical Active Task Selection (HATS), a principled framework that constructs an automated curriculum based on the agent's potential for improvement. HATS introduces a regret-based utility metric measuring the performance gap between the current policy and a competent baseline to guide a bi-level selection process. Specifically, it combines a multi-armed bandit for dynamic function class weighting with an active sampler for parameter replay. Experiments demonstrate that HATS significantly outperforms diversity-driven baselines and achieves superior generalization on real-world tasks with fewer training steps, highlighting the value of quality-based data generation in MetaBBO.