From "Aha Moments" to Controllable Thinking: Toward Meta-Cognitive Reasoning in Large Reasoning Models via Decoupled Reasoning and Control

The article introduces MERA, a meta-cognitive reasoning framework designed for Large Reasoning Models (LRMs) that decouples reasoning from control to enhance efficiency and accuracy. MERA utilizes a takeover-based pipeline to generate high-quality reasoning-control supervision data and employs Control-Segment Policy Optimization (CSPO) for training, allowing for independent optimization of control strategies. This approach addresses the issue of redundant reasoning in LRMs, reducing inference costs and latency, which is crucial for practical deployment in AI applications.