Iterative refinement of latent states through looped computation offers a new scaling axis for world models—you can trade off depth for parameter efficiency without sacrificing simulation quality.
This paper introduces Looped World Models, which use parameter-shared transformer blocks that iteratively refine latent states instead of stacking deep layers. This approach achieves 100x parameter efficiency while maintaining faithful long-horizon simulation, and introduces adaptive computation that automatically adjusts depth based on prediction complexity.