| --- |
| license: mit |
| library_name: pytorch |
| tags: |
| - looped-transformer |
| - recursive-reasoning |
| - sudoku |
| datasets: |
| - sapientinc/sudoku-extreme |
| --- |
| |
| # InfiLoop: Sudoku-Extreme |
|
|
| The 6.83M-parameter InfiLoop model from [Scaling to Tens of Thousands of Test-Time Iterations with Loop-Native Attention Residuals](https://arxiv.org/abs/2610.11570), trained on Sudoku-Extreme. This is the EMA checkpoint at step 71,610 used for the paper's Sudoku results. |
|
|
| Code: [github.com/pixeli99/InfiLoop](https://github.com/pixeli99/InfiLoop/tree/main/sudoku) |
|
|
| ## Usage |
|
|
| ```bash |
| git clone https://github.com/pixeli99/InfiLoop.git && cd InfiLoop/sudoku |
| pip install -r requirements.txt |
| python data.py --output data/sudoku |
| hf download pengxiang/InfiLoop-Sudoku model.pt --local-dir checkpoints |
| |
| # evaluation across depths (paper: 97.9% exact accuracy) |
| python verify.py --data data/sudoku --checkpoint checkpoints/model.pt --output verified.json |
| |
| # fixed-depth evaluation, 1,872 Transformer layers |
| python run.py evaluate --data data/sudoku --checkpoint checkpoints/model.pt \ |
| --h-cycles 6 --l-cycles 12 --segments 12 |
| ``` |
|
|
| ## Citation |
|
|
| ```bibtex |
| @article{li2026infiloop, |
| title = {Scaling to Tens of Thousands of Test-Time Iterations with Loop-Native Attention Residuals}, |
| author = {Li, Pengxiang and Muhtar, Dilxat and He, Di and Su, Guinan and Yin, Lu and Liu, Shiwei}, |
| journal = {arXiv preprint arXiv:2610.11570}, |
| year = {2026} |
| } |
| ``` |
|
|
