In this paper, we survey recent advances in meta-reasoning for Large Reasoning Models (LRMs). Although LRMs have made remarkable progress, they face critical limitations including overthinking on simple queries, under-thinking on complex problems, and ineffective exploration lacking strategic direction. We examine how meta-reasoning addresses these challenges by providing the necessary mechanisms to dynamically regulate and optimize the reasoning process, and propose a framework to categorize existing studies into three core components: the monitor which provides self-perception of the reasoning state, the controller which dictates the meta-policy for regulating actions, and the mechanism which executes the algorithmic mapping between them. By systematically reviewing these developments and foundational challenges, we aim to map the current landscape and identify promising directions for creating more adaptive, efficient, and self-regulating reasoning systems.