过去几年，我们普遍沿用自回归的经验来设置 Encoder 的训练预算，而论文给出的闭式解表明，两者的最优配比不在同一个数量级。这意味着，在很多场景里，Encoder 的训练消耗明显超出了最佳区间。

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近日，来自 Yann LeCun 团队（ FAIR×NYU ）在 EMNLP 2025 上发表了一篇极具颠覆性的论文，探讨了 Transformer 架构中 Encoder 部分的最优训练策略。这项研究的核心在于，过去五年我们沿用的 Encoder 训练方法，特别是基于 BERT 的系列模型，可能长期处于“过度训练”状态，算力投入远超必要。这项研究成果对于 ...

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