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This paper introduces Capability Self-Assessment (CSA) for LLMs, formulating it as a policy-learning problem. Experiments show that reinforcement learning effectively teaches models to recognize their own limits and delegate queries they cannot solve, outperforming supervised fine-tuning and generalizing well out-of-distribution.