Researchers propose a method using Reinforcement Learning with metacognitive feedback to address systemic deficiencies in LLMs, such as high-confidence hallucinations and the inability to recognize knowledge boundaries. The approach aims to improve trustworthiness and reliability by enabling models to better monitor and represent their internal uncertainty. This framework focuses on enhancing the model's ability to regulate its own cognitive processes during task performance.