TRM Model for Sudoku Solving
Model Description
This is a Tiny Recursive Model (TRM) fine-tuned for solving Sudoku puzzles. The model uses recursive reasoning to fill in missing numbers in Sudoku grids.
- Developed by: alphaXiv
- Model type: TRM-MLP
- Language(s) (NLP): N/A (grid-based reasoning)
- License: MIT
- Finetuned from model: Custom TRM architecture
Intended Use
Primary Use
This model is designed to solve Sudoku puzzles by predicting the correct numbers for empty cells in standard 9x9 Sudoku grids.
Out-of-Scope Use
Not intended for general NLP tasks, image processing, or other puzzle types.
Limitations and Bias
- Trained only on standard 9x9 Sudoku puzzles
- May not handle non-standard Sudoku variants
- Performance depends on puzzle difficulty
Training Data
The model was trained on a dataset of Sudoku puzzles with extreme difficulty levels. The dataset includes:
- Partially filled 9x9 grids
- Correct solutions
- Difficulty ratings
Evaluation Results
| Variant | Metric | Claimed | Achieved |
|---|
| TRM-MLP | Accuracy | 87.4% | 79.37% ± 0.12% |
| TRM-Attention | Accuracy | 74.7% | 73.66% ± 0.13% |
Results from independent reproduction study.
Repository
