metadata
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, 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
Usage
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
@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}
}