Papers › Generating Long Sequences with Sparse Transformers
Generating Long Sequences with Sparse Transformers
Rewon Child, Scott Gray, Alec Radford, Ilya Sutskever
Transformers are powerful sequence models, but require time and memory that grows quadratically with the sequence length. In this paper we introduce sparse factorizations of the attention matrix which reduce this to O(n √(n)). We also introduce a) a variation on architecture and initialization to train deeper networks, b) the recomputation of attention matrices to save memory, and c) fast attention kernels for training. We call networks with these changes Sparse Transformers, and show they can model sequences tens of thousands of timesteps long using hundreds of layers. We use the same architecture to model images, audio, and text from raw bytes, setting a new state of the art for density modeling of Enwik8, CIFAR-10, and ImageNet-64. We generate unconditional samples that demonstrate global coherence and great diversity, and show it is possible in principle to use self-attention to model sequences of length one million or more.
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Code
Syntology Ran 5 of 6 code samples harvested from 2 repositories linked to this paper; 1 has no recorded run. Of those that ran: 1 ran · our draft was wrong; 4 ran with no contract checked.
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Code Syntology ran Syntology
6 samples harvested; 5 ran; 0 honoured the contract we drafted; 1 has no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Audio Generation | Classical music, 5 seconds at 12 kHz | Sparse Transformer 152M (strided) | Bits per byte | 1.97 | #1 of 2 | Archive leaderboard | report |
| Image Generation | ImageNet 64x64 | Sparse Transformer 59M (strided) | Bits per dim | 3.44 | #43 of 65 | Archive leaderboard | report |
| Language Modelling | enwik8 | Sparse Transformer (30 layers, fixed attn) | Bit per Character (BPC) | 0.99 | #14 of 42 | Archive leaderboard | report |
| Language Modelling | enwik8 | Sparse Transformer (30 layers, fixed attn) | Number of params | 95M | #14 of 42 | Archive leaderboard | report |
| Open-Domain Question Answering | SearchQA | Sparse Attention | EM | 64.7 | #4 of 14 | Archive leaderboard | report |
| Question Answering | Natural Questions (long) | Sparse Attention | F1 | 74.5 | #4 of 13 | Archive leaderboard | report |
| Question Answering | Quasart-T | Sparse Attention | EM | 52.1 | #3 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
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