Problem-solving data in continued pretraining improves LLM math reasoning more than general math corpora, and tutorship amplification is the most effective synthesis method.
Physics of language models: Part 3.1, knowledge storage and extraction
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Advancing Mathematical Reasoning in Language Models: The Impact of Problem-Solving Data, Data Synthesis Methods, and Training Stages
Problem-solving data in continued pretraining improves LLM math reasoning more than general math corpora, and tutorship amplification is the most effective synthesis method.