翁荔Scaling Law博文解读
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https://www.youtube.com/watch?v=o0Bl3zeDfJM 博客原文地址 https://lilianweng.github.io/posts/2026-06-24-scaling-laws/ 先通俗易懂的解读这篇硬核博客 👇 🎯 一句话版 模型越大、数据越多、算得越久,AI 就越聪明——而且变好的速度和规模之间,大致遵循一条"幂律曲线"。 但到底"模型"和"数据"谁该先加大?这是整篇文章争论的核心。 🍳 用一个做菜类比 假设你在练一个超牛大厨(= 训练 AI): 模型参数量 N = 厨师脑容量(记菜谱、技巧的能力) 数据量 D = 你让他练过的菜品份数 计算量 C = 总时间 + 灶台费(钱/电) 🔹 Kaplan(2020)说: "脑子大的厨师,学得快,所以给同样预算, 优先把脑容量搞大,少练几道菜也行 。" → 结果:早期大模型都偏小数据量训练(后来发现——其实练少了)。 🔹 Chinchilla(2022)反驳: "不对! 脑子和练习题要一起加 ,模型翻倍,题也要翻倍,效果才最好。" → 用更小模型 + 多训 4 倍…
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Coding AI shifts how fast software ships and how much human review each change needs. DEV — ML reports that 翁荔Scaling Law博文解读
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