RIME: Reliable Imagination for Meta-Black-Box Optimization

Meta-black-box optimization (MetaBBO) learns optimizer configuration from optimization trajectories. Surrogate-assisted training reduces the cost of collecting optimization trajectories by replacing expensive objective evaluations with surrogate predictions. However, existing methods use the collected transitions only for direct policy updates and do not learn the optimizer dynamics needed to generate additional training experience. A learned dynamics model can reuse these trajectories through imagined transitions, provided its predictions are reliable. This is challenging because the collected trajectories already reflect surrogate approximation errors, while errors in predicted dynamics can accumulate over imagined rollouts. We propose RIME, a MetaBBO algorithm that learns optimizer configuration through task-conditioned, uncertainty-gated imagination. We first learn a task representation through objective-surrogate pretraining to provide shared context for optimizer control and dynamics prediction. We then learn a task-conditioned world model that reuses collected trajectories through imagined experience, with predictive uncertainty determining which transitions are trusted and the length of imagined rollouts. RIME achieves the best average rank on the evaluated BBOB benchmark across five dimensionalities and outperforms most MetaBBO baselines on UAV path-planning tasks.