The universal covering of Super Mario 64s maps[^1] is actually what Bismuth describes in his video: “Super Mario 64 Tool-assisted speedrun world record explained”. The worlds of SM64 or rather the maps in Super Mario 64, are all homeomorphic to \(T^3\) (meaning they are pretty much the same, see the definition of homeomorphisms). The \(T^3\) is the 3-dimensional torus, which is the product of three circles. Look at this short clip from his video:
If the exported model behaves worse in another runtime, Unsloth flags the most common cause: wrong chat template / EOS token at inference time (you must use the same chat template you trained with).
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在这个过程中，其实在强化学习和神经网络的这个深度学习之间，又发生了两个学派之争，其实就是杰弗里·辛顿（Geoffrey Hinton）和理查德·萨顿（Richard Sutton），非常有意思。这两个人都在加拿大，辛顿在多伦多大学，萨顿在阿尔伯塔大学。萨顿是强化学习之父。大卫·西尔弗知道了这个萨顿是强化学习的理论之后，跟他的想法非常近似，他就立刻跑到阿尔伯塔，那个冰天雪地去读他的研究生去了。