paper introduces the Reformer, a more efficient Transformer model. It

achieves this through three key improvements: replacing dot-product

attention with locality-sensitive hashing for faster computation on long

sequences, utilizing reversible residual layers to reduce memory

consumption by storing activations only once, and employing a chunking

mechanism to further optimize memory usage in feed-forward layers. The

Reformer maintains performance comparable to standard Transformers while

significantly improving speed and memory efficiency, especially when

processing lengthy sequences. Experimental results across text and

image generation tasks demonstrate its superior performance.