Unsupervised Library Learning of Typed Motor Programs

REFACTOR-VLA introduces a novel 'wake/sleep' architecture for unsupervised learning of reusable skills in vision-language-action (VLA) models. It uses a Behavioral-Equivalence Kernel (BEK) based on a learned latent world model to cluster action segments into skills. These skills are represented as typed lambda terms and used by a library-conditioned rectified-flow action decoder. Skills are accepted based on Minimum Description Length (MDL) and return-preservation criteria. The system employs a three-phase training schedule: world-model warmup, wake-phase policy optimization, and sleep-phase skill discovery. Empirical results show that increasing world model size does not always improve performance, and an auxiliary supervised contrastive loss during warmup significantly enhances skill clustering quality.