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English(EN) What Really Improves Mathematical Reasoning: Structured Reasoning Signals Beyond Pure Code

代码并不能提高LLM的数学推理能力;结构化追踪可以

一篇新的研究论文探讨了代码对大型语言模型数学推理能力的影响。研究发现,虽然代码可以提高编程能力,但它并不能普遍增强数学推理能力,甚至可能与知识密集型任务产生竞争。研究人员发现,结构化推理追踪(如数学-文本混合)比单独的可执行代码更能有效地提高推理能力。他们建议,增加结构化数学领域样本的密度,可以提供一种有针对性的方法来提升数学推理能力,而不会牺牲编程性能。 AI

影响 阐明了哪些数据特征可以提高LLM的推理能力,并提出了更精确的数据中心优化策略。

排序理由 详细介绍LLM训练数据研究结果的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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代码并不能提高LLM的数学推理能力;结构化追踪可以

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详细介绍LLM训练数据研究结果的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Enhong Chen ·

    什么真正能提升数学推理能力:结构化推理信号优于纯代码

    Code has become a standard component of modern foundation language model (LM) training, yet its role beyond programming remains unclear. We revisit the claim that code improves reasoning through controlled pretraining experiments on a 10T-token corpus with fine-grained domain sep…