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English(EN) Everything in Moderation: Per-Domain Coverage Optima and Alignment-Resistant Domain Gaps in Multi-Domain Mid-Training

研究发现:大型语言模型中期训练数据构成影响性能

研究人员探索了大型语言模型的中期训练阶段,发现跨不同领域的最佳性能是通过适度的(10%-40%)数据构成而非极端分配来实现的。这种最佳构成在随后的对齐阶段后仍然保持稳健且基本不变,表明中期训练的决策对模型的最终能力有显著影响。研究使用了Qwen3-8B-Base模型和KOR-Bench数据集来分析这些影响。 AI

影响 研究结果表明,中期训练期间仔细的数据构成对于大型语言模型的性能至关重要,并可能影响未来的训练方法。

排序理由 学术论文,详细介绍关于大型语言模型训练的研究发现。[lever_c_demoted from research: ic=1 ai=1.0]

在 Hugging Face Daily Papers 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

研究发现:大型语言模型中期训练数据构成影响性能

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学术论文,详细介绍关于大型语言模型训练的研究发现。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    万事皆有度:多领域中期训练中的每域覆盖最优解与抗对齐领域差距

    Mid-training, the stage between pre-training and alignment, is where a model's per-domain data composition is typically set by data availability rather than principled design. We ask what that decision buys, and whether a later alignment pass can undo it. In a controlled logical-…