MetaCtrl is a lightweight controller that adaptively regulates large language models (LLMs) to improve their reasoning accuracy while reducing inference-time generation. It observes the evolving reasoning trace, decides whether to continue, simplify, or conclude reasoning, and is trained using reinforcement learning. MetaCtrl improves the accuracy of LLMs on various benchmarks, including mathematics, science, and code, and can transfer to unseen reasoners without further training.