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重新思考在测试时计算扩展下的微调：限制置信度改善数学推理

💡 原文中文，约500字，阅读约需2分钟。

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内容提要

本研究探讨了如何优化大型语言模型（LLMs）的训练，以适应测试时的计算策略。结果表明，交叉熵损失函数可能导致模型过于自信，从而影响准确性。通过限制置信度，提出了一种改进的训练损失，显著提升了数学推理能力。

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