Reversible Residual Layers

The Reformer - Pushing the limits of language modeling

This Hugging Face blog post covers the Reformer, a memory-efficient transformer architecture that uses locality-sensitive hashing (LSH) attention and reversible residual layers to handle very long sequences. The post explains the technical mechanisms that allow Reformer to process sequences up to 1 million tokens with significantly reduced memory footprint compared to standard transformers. It serves as an educational deep-dive into the architectural innovations introduced in the original Reformer paper by Kitaev et al.