自动课程方法有望降低大型语言模型推理训练成本

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-28 04:00

研究人员开发了一种自动课程方法，可显著降低训练大型语言模型进行推理任务的成本。该方法利用模型自身的表现来动态选择训练问题，从而优化学习过程。研究表明，自动课程方法在监督微调中需要指数级减少的推理演示，并在强化学习微调中将计算成本与目标精度解耦。 AI

影响自动课程方法可能会大大降低训练大型语言模型高级推理能力的计算和数据成本。

排序理由研究论文发布在arXiv上，详细介绍了大型语言模型的新训练方法。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv stat.ML TIER_1 English(EN) · Nived Rajaraman, Audrey Huang, Miro Dudik, Robert Schapire, Dylan J. Foster, Akshay Krishnamurthy · 2026-08-28 04:00

学习推理与课程 I：自动课程的可证明优势

arXiv:2603.18325v2 Announce Type: replace-cross Abstract: Chain-of-thought reasoning, where language models expend additional computation by producing thinking tokens prior to final responses, has driven significant advances in model capabilities. However, training these reasonin…