Recurrence in Looped Language Models Affects Reasoning and Knowledge

Experiments on Looped LMs show that increasing computational depth through parameter sharing improves reasoning beyond the training horizon but can degrade knowledge performance. Effectiveness is highly dependent on the specific task and inference budget.