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Transformers have demonstrated effectiveness in in-context solvingdata-fitting problems from various (latent) models, as reported by Garg et al.However, the absence of an inherent iterative structure in the transformerarchitecture presents a challenge in emulating the iterative algorithms, whichare commonly employed in traditional machine learning methods. To address this,we propose the utilization of looped transformer architecture and itsassociated training methodology, with the aim of incorporating iterativecharacteristics into the transformer architectures. Experimental resultssuggest that the looped transformer achieves performance comparable to thestandard transformer in solving various data-fitting problems, while utilizingless than 10% of the parameter count.

Looped Transformers are Better at Learning Learning Algorithms