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license: apache-2.0
task_categories:
- question-answering
tags:
- sudoku
- reasoning
- hierarchical-reasoning-model
- puzzle-solving
pretty_name: Sudoku-Extreme & Augmented Datasets for HRM Reproduction
---
# Sudoku-Extreme Datasets for HRM Reproduction
Preprocessed datasets used for reproducing the **Hierarchical Reasoning Model (HRM)** and **Augmented HRM** papers on the Sudoku-Extreme benchmark.
## Datasets
| Dataset | Description | Train Examples | Test Examples |
|---------|-------------|---------------|--------------|
| `sudoku-extreme-1k-aug-1000` | Vanilla Sudoku-Extreme (1000 puzzles, 1000x augmented) | 1,001,000 | 422,786 |
| `sudoku-extreme-1k-aug-1000-hint` | Augmented version with easier puzzles mixed in | 2,002,000 | 422,786 |
## Source Papers
- **HRM**: [Hierarchical Reasoning Model](https://arxiv.org/abs/2506.21734) (Wang et al., 2025)
- **Augmented HRM**: [Are Your Reasoning Models Reasoning or Guessing?](https://arxiv.org/abs/2601.10679) (Ren & Liu, 2026)
## Dataset Format
Each dataset contains `train/` and `test/` directories with:
- `all__inputs.npy` — Puzzle inputs, shape `(N, 81)`, values 1-10
- `all__labels.npy` — Solution labels, shape `(N, 81)`, values 2-10
- `all__puzzle_indices.npy` — Cumulative indices marking puzzle boundaries
- `all__puzzle_identifiers.npy` — Puzzle type IDs
- `all__group_indices.npy` — Cumulative group boundary indices
- `dataset.json` — Metadata (vocab_size=11, seq_len=81, pad_id=0)
## How to Build From Scratch
```bash
# Vanilla dataset
python dataset/build_sudoku_dataset.py --output-dir data/sudoku-extreme-1k-aug-1000 --subsample-size 1000 --num-aug 1000
# Augmented (hint) dataset
python dataset/build_sudoku_dataset.py --output-dir data/sudoku-extreme-1k-aug-1000-hint --subsample-size 1000 --num-aug 1000 --hint
|