State space models, emergence, and ergodicity: How many parameters are needed for stable predictions?
<|reference_start|>State space models, emergence, and ergodicity: How many parameters are needed for stable predictions?: 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 in $\mathbb{R}$. For this situation, we show that there exists no learner using a linear filter which can succesfully learn the random walk unless the filter length exceeds a certain threshold depending on the effective memory length and horizon of the problem.<|reference_end|>
@article{ziemann2024state,
    title={State space models, emergence, and ergodicity: How many parameters are
  needed for stable predictions?},
    author={Ingvar Ziemann, Nikolai Matni and George J. Pappas},
    journal={arXiv preprint arXiv:2409.13421},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.13421},
    primaryClass={cs.LG cs.SY eess.SY}
}