Bridging Kolmogorov Complexity and Deep Learning: Asymptotically Optimal Description Length Objectives for Transformers

Researchers introduce a theoretical framework connecting Kolmogorov complexity to Transformer neural networks through asymptotically optimal description length objectives. The work demonstrates computational universality of Transformers and proposes a variational objective that achieves optimal compression, though current optimization methods struggle to find such solutions from random initialization.