Hyperloop Transformers
LLM architecture research generally aims to maximize model quality subject to fixed compute/latency budgets. However, many applications of interest such as edge and on-device deployment are further constrained by the model's memory footprint, thus motivating parameter-efficient architectures for language modeling. This paper describes a simple architecture that improves the parameter-efficiency of LLMs. Our architecture makes use of looped Transformers as a core primitive, which reuse Transformer layers across depth and are thus more parameter-efficient than ordinary (depth-matched) Transformers. We organize the looped Transformer into three blocks--begin, middle, and end blocks--where ea...
| Authors | Abbas Zeitoun, Lucas Torroba-Hennigen, Yoon Kim |
|---|---|
| Primary category | cs.LG |
| Published | 23 April 2026 |
| Last updated at source | 2 July 2026 |
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