arXiv:2610.20949v1 Announce Type: new Abstract: O-RAN enables the deployment of heterogeneous control and provisioning algorithms through open interfaces and programmable intelligent controllers. However, most existing intelligent O-RAN solutions rely on a single optimization strategy designed for a fixed operational objective, limiting their ability to adapt when network conditions or operator priorities change. This paper proposes an objective-aware policy orchestration framework that selects the most appropriate provisioning policy for each control window based on the current network context and operator objective. The framework orchestrates a portfolio of heterogeneous provisioning experts using contextual-bandit learning. Two policy selectors, Linear Thompson Sampling and a Bootstrap Tree ensemble, are evaluated against static provisioning policies and a deterministic rule-based orchestrator. Experimental results show that learning-based policy orchestration can effectively adapt policy selection to varying operating conditions, improving overall objective-aware performance while preserving the modularity of existing provisioning algorithms.