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INSPIRE: An Internalize-Then-Improve Approach for Example-Driven Mathematical Reasoning
Mathematical reasoning has seen rapid progress in large language models (LLMs), yet existing methods optimize predominantly for final-answer correctness, raising the question whether models truly internalize mathematical concepts or merely memorize solution patterns. In human mathematics education,
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