Summarizing https://arxiv.org/pdf/2307.08621.pdf

Here's my try:

This paper proposes RETNET, a foundation architecture for large language models that achieves training parallelism, low-cost deployment, and good performance. The proposed mechanism can be written as recurrent neural networks or parallel representation, which is favorable for inference. Experimental results show that RETNET outperforms the Transformer model in terms of scaling, parallel training, low-cost deployment, and efficient inference.