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Compressive Transformer

3 papers tagged archive 2025-07-28

Introduced by Jack W. Rae et al. in Compressive Transformers for Long-Range Sequence Modelling

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

The Compressive Transformer is an extension to the Transformer which maps past hidden activations (memories) to a smaller set of compressed representations (compressed memories). The Compressive Transformer uses the same attention mechanism over its set of memories and compressed memories, learning to query both its short-term granular memory and longer-term coarse memory. It builds on the ideas of Transformer-XL which maintains a memory of past activations at each layer to preserve a longer history of context. The Transformer-XL discards past activations when they become sufficiently old (controlled by the size of the memory). The key principle of the Compressive Transformer is to compress these old memories, instead of discarding them, and store them in an additional compressed memory.

At each time step t, we discard the oldest compressed memories (FIFO) and then the oldest n states from ordinary memory are compressed and shifted to the new slot in compressed memory. During training, the compressive memory component is optimized separately from the main language model (separate training loop).

PaperSourceSee Code · lucidrains/compressive-transformer-pytorch

Papers archive 2025-07-28

3 shown of 3, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

5 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Data Visualization1
Dimensionality Reduction1
Embeddings Evaluation1
Language Modelling1
Sentence1

Usage over time archive 2025-07-28

Papers per year tagged with Compressive Transformer: 2019 to 2023, peak 1 1 0 2019: 1 paper 2019 2020: 0 papers 2020 2021: 1 paper 2021 2022: 0 papers 2022 2023: 1 paper 2023
Papers per year the archive tags with this method, by the paper's archive date (3 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Transformers

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