"Hyperloop Transformers" This paper propose a memory-efficient LLM via looped Transformers. They basically reuse the middle block across depth, then add hyper-connections only between loops. Key result is that this restores flexibility lost from weight sharing, letting the model beat depth-matched Transformers with ~50% fewer parameters. The result still holds after INT4 quantization too.