There are plenty of different way to learn, and the optimal probably depend on the material to learn. For example, in machine learning, there is something call

There are plenty of different way to learn, and the optimal probably depend on the material to learn. For example, in machine learning, there is something called stochastic gradient descent, where to learn you present a single random element at a time from the dataset. In the end it will have learned of all concepts, by becoming more and more confident in each individual concepts. For example to learn QM, you pick a random QM wikipedia article, and try to push through the article, even though there are some things you don't understand. Then you do the same thing, for a different unrelated QM article. For learning tennis, you don't learn specifically forehand, then learning backhand, but you alternate them at random so that you have a single unified way of playing with smooth transitions, instead of having to switch between different "modes" of thinking. Sure more memory can allow some speed-space trade-off in learning ability, but using your memory too much may make you miss some fluency that may have emerged. For example the old-school of machine learning was using databases and K-near neighbors, which used a lot of memory and was slow. But the new-school of machine learning are using constant memory algorithm and compressing the data in it, and it can learn to generate all the pictures in the world with only 4 Gb of weights. Learning is imagining, once you bootstrap your imagination, its bandwidth to synthesize new examples from which you can learn from, is much greater than the bandwidth of looking up new data material to learn from.