Dynamic Parameter Reuse Augments Reasoning via Latent Chain of Thought

Standard language models often rely on massive parameter counts for their performance, utilizing each parameter only once per inference pass. This prompts consideration of recurrent structures, where models reuse parameters across sequential time, depth, or training progression to achieve improved performance and reduced training cost. We draw connections in the landscape of parameter reuse, from growing models via stacking to recurrent looping, and postulate that these architectural priors act as a form of Latent Chain of Thought (LCoT), allowing models to reason in a continuous state space. By shifting towards deeper and dynamic computation, grown and recurrent architectures offer a path toward improved reasoning in compact networks, ascending beyond scaling laws of standard architectures.