RLT (wip)
Unofficial implementation of the Recurrent Looped Transformer proposed by Yifan Zhang of Princeton.
Will also do some exploration of the Recurrent Transformer proposed by Costin-Andrei Oncescu et al. of Harvard, if I have any remaining time
Citations
@techreport{zhang2026recurrentlooped,
title = {Recurrent Looped Transformer},
author = {Zhang, Yifan},
year = {2026},
month = {Sep},
url = {https://github.com/yifanzhang-pro/recurrent-looped-tranformer}
}
@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},
}
1 maintainer
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
rlt_pytorch-0.0.6.tar.gz
(7.3 kB
view details)
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file rlt_pytorch-0.0.6.tar.gz.
File metadata
- Download URL: rlt_pytorch-0.0.6.tar.gz
- Upload date:
- Size: 7.3 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via:
uv/0.8.17
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
6bf3d985b38b121529aba2c721ac45b18b07df4e7fb4e0cadb6af99b769755be
|
|
| MD5 |
8c3ea94112ebf2cb47abd66810f6aff3
|
|
| BLAKE2b-256 |
2a3671a1552cd32577c6d509088620980984c913c304b35740a595e42536b530
|
File details
Details for the file rlt_pytorch-0.0.6-py3-none-any.whl.
File metadata
- Download URL: rlt_pytorch-0.0.6-py3-none-any.whl
- Upload date:
- Size: 6.6 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via:
uv/0.8.17
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
823ae58b0c1332a6fe9bccce98ecde2b64a5533d67037bdfde2a34473b0fa0e5
|
|
| MD5 |
2d015d45f6cdc966b2309be5318bcc1b
|
|
| BLAKE2b-256 |
c342fab274cd6d0b3c8d4e8d48c62488923db6598110c645265243e3fad1e85d
|
