Papers › Reformer: The Efficient Transformer
Reformer: The Efficient Transformer
Nikita Kitaev, Łukasz Kaiser, Anselm Levskaya
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(L²) to O(LlogL), 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.
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Code
Syntology Ran 6 of 8 code samples harvested from 1 repository linked to this paper; 2 have no recorded run. Of those that ran: 5 ran · our draft was wrong; 1 ran with no contract checked.
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Code Syntology ran Syntology
8 samples harvested; 6 ran; 0 honoured the contract we drafted; 2 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
Licence: 0 of the 8 samples are pointer only, meaning Syntology does not serve that copy's text. This page shows no code text for any sample; each one links to its file in the repository.
Harvested from Rick-McCoy/Reformer-pytorch. “Ran” means the sample executed on a synthesized input. It does not mean the output is correct, and nothing here reproduces the paper's results. “Honoured” and “violated” refer to a contract Syntology drafted from the code itself; “our draft was wrong” and “fixture could not drive it” are failures of Syntology's instrument, not of the code.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| D4RL | D4RL | Reformer | Average Reward | 63.9 | #8 of 9 | Archive leaderboard | report |
| Image Generation | ImageNet 64x64 | Reformer (12 layers) | Bits per dim | 3.710 | #56 of 65 | Archive leaderboard | report |
| Image Generation | ImageNet 64x64 | Reformer (6 layers) | Bits per dim | 3.740 | #58 of 65 | Archive leaderboard | report |
| Language Modelling | WikiText-103 | Reformer 125M | Test perplexity | 26.0 | #62 of 89 | Archive leaderboard | report |
| Open-Domain Question Answering | SearchQA | Locality-Sensitive Hashing | EM | 66.0 | #2 of 14 | Archive leaderboard | report |
| Question Answering | Natural Questions (long) | Locality-Sensitive Hashing | F1 | 75.5 | #3 of 13 | Archive leaderboard | report |
| Question Answering | Quasart-T | Locality-Sensitive Hashing | EM | 53.2 | #2 of 7 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
Introduced by this paper: Reformer
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