Papers › Compressive Transformers for Long-Range Sequence Modelling
Compressive Transformers for Long-Range Sequence Modelling
Jack W. Rae, Anna Potapenko, Siddhant M. Jayakumar, Timothy P. Lillicrap
We present the Compressive Transformer, an attentive sequence model which compresses past memories for long-range sequence learning. We find the Compressive Transformer obtains state-of-the-art language modelling results in the WikiText-103 and Enwik8 benchmarks, achieving 17.1 ppl and 0.97 bpc respectively. We also find it can model high-frequency speech effectively and can be used as a memory mechanism for RL, demonstrated on an object matching task. To promote the domain of long-range sequence learning, we propose a new open-vocabulary language modelling benchmark derived from books, PG-19.
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Code
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Tasks
Datasets
Introduced by this paper, per the archive.
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Language Modelling | Hutter Prize | Compressive Transformer | Bit per Character (BPC) | 0.97 | #2 of 18 | Archive leaderboard | report |
| Language Modelling | WikiText-103 | Compressive Transformer (18L, M=1024) | Test perplexity | 17.1 | #20 of 89 | Archive leaderboard | report |
| Language Modelling | WikiText-103 | Compressive Transformer (18L, M=1024) | Validation perplexity | 16.0 | #20 of 89 | Archive leaderboard | report |
| Language Modelling | enwik8 | Compressive Transformer (24 layers) | Bit per Character (BPC) | 0.97 | #8 of 42 | Archive leaderboard | report |
| Language Modelling | enwik8 | Compressive Transformer (24 layers) | Number of params | 277M | #8 of 42 | 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: Compressed Memory, Compressive Transformer
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