# Reformer: The Efficient Transformer

**Authors:** Nikita Kitaev, Łukasz Kaiser, Anselm Levskaya  
**arXiv:** [2001.04451](https://arxiv.org/abs/2001.04451)  
**Date:** 2020-01-13  
**Summary page:** https://www.summarizepaper.com/fr/paper/2001.04451v1/

## Abstract

Large Transformer models routinely achieve state-of-the-art results on a number of tasks but training these models can be prohibitively costly, especially on long sequences. We introduce two techniques to improve the efficiency of Transformers. For one, we replace dot-product attention by one that uses locality-sensitive hashing, changing its complexity from O($L^2$) to O($L\log L$), where $L$ is the length of the sequence. Furthermore, we use reversible residual layers instead of the standard residuals, which allows storing activations only once in the training process instead of $N$ times, where $N$ is the number of layers. The resulting model, the Reformer, performs on par with Transformer models while being much more memory-efficient and much faster on long sequences.