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.
// 压入当前数，维持栈的递增特性
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// Concatenate pending data with new chunks，更多细节参见im钱包官方下载
* @returns {number[]} nums1每个元素在nums2中对应的「下一个更大元素」，无则返回-1