GLU/SwiGLU 在实际中是门控形式（two linear branches），是向量上的逐元素操作；为了在一维上可视化，我用简化的标量形式来画图 —— 把两条分支都用相同的输入值（即把 a=x, b=x），因此 GLU(x)=x∗sigmoid(x) SwiGLU(x)=x∗SiLU(x) 。这能直观展示门控机制的形状差异。
在真实的商业世界中，所谓“做正确的选择”，是件表态容易，但行动不易的事。它考验的，是企业能否用真金白银，为自己的价值观买单。但至少在巴迪高看来，这件事不应只停留在口号层面。
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let minVal = Infinity;
In recent years, LLMs have shown significant improvements in their overall performance. When they first became mainstream a couple of years before, they were already impressive with their seemingly human-like conversation abilities, but their reasoning always lacked. They were able to describe any sorting algorithm in the style of your favorite author; on the other hand, they weren't able to consistently perform addition. However, they improved significantly, and it's more and more difficult to find examples where they fail to reason. This created the belief that with enough scaling, LLMs will be able to learn general reasoning.