Hybrid-thinking language models expose both cheap non-thinking and expensive thinking modes, creating a deployment problem: deciding when additional reasoning is worth its token cost and override risk. We formulate this problem as budgeted opportunity allocation, where a controller first obtains a cheap seed answer, estimates the marginal value and risk of paid thinking, and preserves the seed unless an adoption gate accepts the thinking result. We propose Bora-Budget, a budget-aware inference framework for frozen LLMs, and instantiate it as Bora-Switch-Calibrated. The controller uses seed-state features to predict helpfulness, harmfulness, and thinking cost, then allocates thinking through either frontier operating points or an AutoThreshold policy calibrated to a total token budget. Across LLM scales on math dataset, the method substantially improves over random allocation and approaches uniform-thinking accuracy under lower token budgets. Transfer results on multiple-choice, code, and hard-QA tasks show promising but uneven generalization: trace length remains a strong baseline, harmful overrides are reduced but not eliminated, and learning how much to think remains open. These results position Bora-Budget as a seed-preserving control layer for budgeted inference rather than a replacement for stronger always-thinking baselines.

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.