#Meta-R1

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Meta-R1 是一项把认知科学元认知理论工程化、并证明在数学推理任务上带来实用收益的工作。它支持“把显式控制/规划机制引入大型推理模型可改善表现与效率”的命题，但也带来了工程复杂度、可迁移性与对抗稳健性的挑战。 总体上，它为将“调制/元控制”作为智能演化方向提供了可操作范式与初步实证。 Large Reasoning Models (LRMs) demonstrate remarkable capabilities on complex tasks, exhibiting emergent, human-like thinking patterns. Despite their advances, we identify a fundamental limitation: current LRMs lack a dedicated meta-level cognitive system—an essential faculty in human cognition that enables “thinking about thinking”. This absence leaves their emergent abilities uncontrollable (non-adaptive reasoning), unreliable (intermediate error), and inflexible (lack of a clear methodology). To address this gap, we introduce Meta-R1, a systematic and generic framework that endows LRMs with explicit metacognitive capabilities. Drawing on principles from cognitive science, Meta-R1 decomposes the reasoning process into distinct object-level and meta-level components, orchestrating proactive planning, online regulation, and adaptive early stopping within a cascaded framework. Experiments on three challenging benchmarks and against eight competitive baselines demonstrate that Meta-R1 is: (I) high-performing, surpassing state-of-the-art methods by up to 27.3%; (II) token-efficient, reducing token consumption to 15.7%∼32.7% and improving efficiency by up to 14.8% when compared to its vanilla counterparts; and (III) transferable, maintaining robust performance across datasets and model backbones.
#Meta-R1 #认知科学 #元认知 #数学推理 #大型推理模型