The Reformer is an efficient Transformer model that improves the efficiency of Transformers by reducing their memory and computational requirements. It introduces two key techniques: (1) replacing dot-product attention with locality-sensitive hashing (LSH) attention, which reduces the complexity from O(L²) to O(L log L), and (2) using reversible residual layers, which allow activations to be stored only once instead of N times, where N is the number of layers. These techniques enable the Reformer to perform as well as standard Transformers while being significantly more memory-efficient and faster on long sequences. The Reformer addresses three major memory and computational challenges in Transformers: (1) the need to store activations for back-propagation across N layers, (2) the high memory usage of deep feed-forward layers due to their large depth, and (3) the high computational and memory complexity of attention on long sequences. To solve these, the Reformer uses reversible layers to eliminate the N factor, splits activations in feed-forward layers to reduce the depth factor, and replaces full attention with LSH attention to reduce the O(L²) factor. LSH attention works by hashing queries and keys into buckets, allowing attention to be computed only within each bucket. This reduces the number of operations needed, making it feasible to process long sequences. The number of hash rounds can be adjusted to balance accuracy and efficiency. Reversible layers allow the model to avoid storing activations for each layer, reducing memory usage. Chunking is used to process feed-forward layers in smaller segments, further reducing memory requirements. Experiments show that the Reformer achieves results comparable to standard Transformers on tasks such as text generation (enwik8) and image generation (imagenet-64), while being much faster and more memory-efficient. The Reformer is particularly effective on long sequences, where standard Transformers struggle due to memory and computational constraints. The model is also efficient in terms of training time and can be trained on a single machine with large sequences. The Reformer's techniques make it a promising solution for large-scale sequence modeling tasks.The Reformer is an efficient Transformer model that improves the efficiency of Transformers by reducing their memory and computational requirements. It introduces two key techniques: (1) replacing dot-product attention with locality-sensitive hashing (LSH) attention, which reduces the complexity from O(L²) to O(L log L), and (2) using reversible residual layers, which allow activations to be stored only once instead of N times, where N is the number of layers. These techniques enable the Reformer to perform as well as standard Transformers while being significantly more memory-efficient and faster on long sequences. The Reformer addresses three major memory and computational challenges in Transformers: (1) the need to store activations for back-propagation across N layers, (2) the high memory usage of deep feed-forward layers due to their large depth, and (3) the high computational and memory complexity of attention on long sequences. To solve these, the Reformer uses reversible layers to eliminate the N factor, splits activations in feed-forward layers to reduce the depth factor, and replaces full attention with LSH attention to reduce the O(L²) factor. LSH attention works by hashing queries and keys into buckets, allowing attention to be computed only within each bucket. This reduces the number of operations needed, making it feasible to process long sequences. The number of hash rounds can be adjusted to balance accuracy and efficiency. Reversible layers allow the model to avoid storing activations for each layer, reducing memory usage. Chunking is used to process feed-forward layers in smaller segments, further reducing memory requirements. Experiments show that the Reformer achieves results comparable to standard Transformers on tasks such as text generation (enwik8) and image generation (imagenet-64), while being much faster and more memory-efficient. The Reformer is particularly effective on long sequences, where standard Transformers struggle due to memory and computational constraints. The model is also efficient in terms of training time and can be trained on a single machine with large sequences. The Reformer's techniques make it a promising solution for large-scale sequence modeling tasks.