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.
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。关于这个话题，safew官方下载提供了深入分析
Beyond this, I think there's a case to be made for designing a new game from the ground up with this architecture. At the very least, gamers who are skeptical about investing their time into a live-service game out of fear of it shutting down could rest easy knowing that the developers have built the game with this failsafe in mind.
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