If the LIMO hypothesis about the existence of a latent capacity for efficient reasoning in small models that can be elicited by finetuning the model with a small datasets is true, then we could see a huge transference of power from huge models to small models and that in a recurrent way seems to offer unlimited power. But to feed that loop there should be a property of those datasets, they teach the model to adapt reasoning to model size and that is verified by the model extending the depth of the reasoning chain using a small branching factor in the exploration space, like a minimum cover to detect deep patterns.