Bridging Spherical Black-Box Optimizers

We are pleased to present our research at ICML 2026, “Bridging Spherical Black-Box Optimizers”. Full Paper: https://arxiv.org/abs/2606.25761 When optimizing through simulators, external APIs, or in reinforcement learning, gradients are often unavailable. Black-Box Optimization (BBO) fills this gap, but the field has been historically split into two categories: Parametric Methods: Algorithms like Evolution Strategies (ES) scale to high dimensions but only find a single solution.