Recurrent Transformer (wip)
Explorations into the Recurrent Transformer proposed by Costin-Andrei Oncescu et al. of Harvard University
Citations
@misc{oncescu2026recurrenttransformergreatereffective,
title = {The Recurrent Transformer: Greater Effective Depth and Efficient Decoding},
author = {Costin-Andrei Oncescu and Depen Morwani and Samy Jelassi and Alexandru Meterez and Mujin Kwun and Sham Kakade},
year = {2026},
eprint = {2604.21215},
archivePrefix = {arXiv},
primaryClass = {cs.LG},
url = {https://arxiv.org/abs/2604.21215},
}
Release files for recurrent-transformer-pytorch 0.0.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| recurrent_transformer_pytorch-0.0.1.tar.gz | 5.3 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| recurrent_transformer_pytorch-0.0.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 10.1 kB
Release files / recurrent_transformer_pytorch-0.0.1.tar.gz
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Release files / recurrent_transformer_pytorch-0.0.1-py3-none-any.whl
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| Tags | Python 3 |
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