State Space Models, Emergence, and Ergodicity: How Many Parameters Are Needed for Stable Predictions?

Abstract

How many parameters are required for a model to execute a given task? It has been argued that large language models, pre-trained via self-supervised learning, exhibit emergent capabilities such as multi-step reasoning as their number of parameters reach a critical scale. In the present work, we explore whether this phenomenon can analogously be replicated in a simple theoretical model. We show that the problem of learning linear dynamical systems–a simple instance of self-supervised learning–exhibits a corresponding phase transition. Namely, for every non-ergodic linear system there exists a critical threshold such that a learner using fewer parameters than said threshold cannot achieve bounded error for large sequence lengths. Put differently, in our model we find that tasks exhibiting substantial long-range correlation require a certain critical number of parameters–a phenomenon akin to emergence. We also investigate the role of the learner’s parametrization and consider a simple version of a linear dynamical system with hidden state—an imperfectly observed random walk on the real line. For this situation, we show that there exists no learner using a linear filter which can successfully learn the random walk unless the filter length exceeds a certain threshold depending on the effective memory length and horizon of the problem.