This project aims to revolutionize Generative Optimization by framing the generation process as a stochastic optimal control problem. Unlike standard generative models that often ignore environmental or structural uncertainties, our approach integrates risk-averse measures and stochastic control theory to ensure robustness. We will leverage distributed optimization techniques to scale these complex control-theoretic formulations across multi-GPU nodes. The core objective is to develop a framework where the "generation" is an optimally controlled trajectory that remains stable and efficient even under significant system uncertainty.