Weight smuggling likely defeats attempts to cap FLOPs per training run

Researchers highlight that attempts to limit floating-point operations (FLOPs) per AI training run can be circumvented through 'weight smuggling,' where models encode extra computation in weights rather than operations. This challenges current strategies aiming to control AI training complexity by capping FLOPs.