Looped Transformers are Better at Learning Learning Algorithms
This work addresses a specific bottleneck in transformer design for iterative tasks, offering a parameter-efficient solution.
The paper tackled the challenge of transformers lacking iterative structure for emulating traditional machine learning algorithms by proposing a looped transformer architecture, achieving comparable performance to standard transformers with less than 10% of the parameters.
Transformers have demonstrated effectiveness in in-context solving data-fitting problems from various (latent) models, as reported by Garg et al. However, the absence of an inherent iterative structure in the transformer architecture presents a challenge in emulating the iterative algorithms, which are commonly employed in traditional machine learning methods. To address this, we propose the utilization of looped transformer architecture and its associated training methodology, with the aim of incorporating iterative characteristics into the transformer architectures. Experimental results suggest that the looped transformer achieves performance comparable to the standard transformer in solving various data-fitting problems, while utilizing less than 10% of the parameter count.