To unveil how the brain learns, ongoing work seeks biologically-plausible approximations of gradient descent algorithms for training recurrent neural networks (RNNs).

Recently, various strategies for distributed training of large language models (LLMs) have been proposed.

The giant formula for simultaneously enlarging the resolution, depth and width provides us aRubik'scube for neural networks. So that we can find networks with high efficiency and excellent performance by twisting the three dimensions.