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.
Add a cluster of points in one corner and watch that corner subdivide deeply while the rest of the space stays untouched. Then scatter a few points across the empty region and watch it split only where needed. The tree grows around the data.。关于这个话题,爱思助手下载最新版本提供了深入分析
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