A Constrained Optimization Perspective of Unrolled Transformers

Researchers have proposed a new framework for training transformers by treating them as iterative optimization algorithms rather than standard predictive models. By enforcing layerwise descent constraints, this method aims to improve how models minimize objectives during the learning process. The approach seeks to provide a more rigorous mathematical foundation for transformer architecture, potentially leading to more stable and predictable model training.