What Makes Looped Transformers Perform Better Than Non-Recursive Ones (Provably)

Recent research published on arXiv investigates the performance differences between looped transformers, referred to as Looped-Attn, and standard, non-recursive transformers, known as Single-Attn. The study provides a provable explanation for why looped transformers tend to outperform their non-recursive counterparts, particularly in complex reasoning tasks. This advantage is analyzed through the lens of the loss landscape geometry, revealing unique dynamics in the training and optimization processes of looped models. By examining these theoretical aspects, the research clarifies the underlying mechanisms that contribute to the improved effectiveness of looped transformers. This work adds to the growing body of evidence supporting the benefits of recursive architectures in transformer models. The findings are significant for advancing the design of more capable AI systems in the field of machine learning.