You can boost performance of frozen models by intelligently looping internal layers at inference time—no retraining needed, just a smarter application strategy based on ODE theory.
This paper shows how to improve pretrained transformer models at test time by looping a middle section of layers without retraining. The key insight is treating layer loops as smaller refinement steps rather than naive repetition, inspired by numerical methods for solving differential equations.