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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.，更多细节参见快连下载-Letsvpn下载
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Standing there, I was suddenly transported back to Italy: the smell of tomatoes simmering in the kitchen, the warmth of the sun, the rhythm of daily life centered around simple, beautiful ingredients. Pasta wasn’t complicated. It was pure, intentional and full of flavor. I realized that what was missing in that aisle wasn’t just quality — it was that feeling.
其中 λ≈1.05，α≈1.67。，详情可参考heLLoword翻译官方下载
暴力做法是对每个位置向右扫描找第一个更大值，O(n²)。可抽象为：元素像一排人，身高为数值。当前人「下一个更大」= 他右侧第一个没被挡住的人（比当前矮的都被挡住）。单调栈用 O(n) 维护「右侧候选更大值」：倒序遍历，弹掉 ≤ 当前的，栈顶即答案，再入栈当前值。