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Large language models (LLMs) have demonstrated exceptional capabilities in complex mathematical reasoning; however, their massive parameter scales impose prohibitive computational costs, limiting their deployment in resource constrained or local environments. To address these challenges, this thesis investigates advanced knowledge distillation methodologies to enhance the mathematical problem-solv
