---
license: other
task_categories:
- text-generation
tags:
- sudoku
- reasoning
- planning
- discrete-diffusion
- out-of-distribution
pretty_name: Sudoku DLM Reasoning
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train.parquet
  - split: validation
    path: data/validation.parquet
  - split: test
    path: data/test.parquet
  - split: test_ood_confirm
    path: data/test_ood_confirm.parquet
---

# Sudoku DLM Reasoning

`stwistzz/Sudoku_DLM_Reasoning` is a deterministic 9x9 Sudoku benchmark for studying masked
diffusion language models, depth, and iterative decoding. Version 1.1.0
trains only on `original`, `r0`, and `r1_4`; `r5_19` is held out for adjacent
difficulty extrapolation.

Version 1.1.0 changes only the deterministic selection seed to `0`
relative to v1.0.2. The raw CSV files, sources, bucket definitions, split quotas,
schema, and validation rules are unchanged; the four Parquet splits are
regenerated deterministically from the same candidate pool.

## Repository layout

- `raw/`: complete 1,415,420-row candidate snapshot used by this protocol
- `data/`: selected, leakage-audited Parquet splits used for experiments
- `metadata/`: source/release hashes, split specification, and audits
- `scripts/`: deterministic builder and independent verifier
- `docs/BUILDING.md`: end-to-end reconstruction instructions
- `docs/DIFFICULTY.md`: operational difficulty definition and quantitative audit

Files under `raw/` are explicitly source data and are not loaded as extra splits.
Only the four `data/*.parquet` paths declared in the card metadata form the
`default` dataset config.

## Splits

| split | original | r0 | r1_4 | r5_19 | total |
|---|---:|---:|---:|---:|---:|
| train | 65,536 | 65,536 | 65,536 | 0 | 196,608 |
| validation | 2,048 | 2,048 | 2,048 | 0 | 6,144 |
| test | 1,000 | 1,000 | 1,000 | 1,000 | 4,000 |
| test_ood_confirm | 0 | 0 | 0 | 4,000 | 4,000 |

The primary zero-shot protocol selects the checkpoint, decoding strategy, and
number of decoding steps using only `validation`. It must not use `r5_19` before
the final `test` evaluation. `test_ood_confirm` is reserved for confirming a
small number of already-selected configurations; it is not a tuning split.

## Difficulty

`r0`, `r1_4`, and `r5_19` use an upstream Tdoku search measurement. In public
Tdoku commit `af426180dc53aef89b82868e7b3fdfcf42165654`, the counter increments when the
solver expands a binary band/stack-configuration decision after constraint propagation.
It is therefore best interpreted as visited search decision nodes, not recursion
depth, failed undo count, clue count, or human difficulty. `original` comes from
a separate easy collection and has no official rating on this scale.

See `docs/DIFFICULTY.md` for the DFS root, propagation and branch semantics,
bucket formulas, full raw-pool statistics, source confounding, and the upstream
rating-provenance limitation.

The evaluation sample deliberately covers subranges:

- `r1_4`: `[1,2)`, `[2,3)`, `[3,4)`, `[4,5)`
- `r5_19`: `[5,8)`, `[8,12)`, `[12,16)`, `[16,20)`

The 1,000-row test allocation uses 250 rows from each subrange. The 4,000-row
confirmation allocation uses 1,000 rows from each `r5_19` subrange. Within a
subrange, source collection and clue count retain their upstream proportions.

## Deterministic construction

- Upstream repository: [`fhyfhy/diffusion-vs-ar-hard-sudoku`](https://huggingface.co/datasets/fhyfhy/diffusion-vs-ar-hard-sudoku)
- Pinned upstream revision: `527859f62c745c16833aded130ad9f9ddddb76af`
- Release seed: `0`
- Sampling: proportional metadata-stratified bottom-k by SHA-256 rank
- Leakage checks: exact puzzle plus digit-renaming-normalized puzzle/solution pair
- Validation: 81-character format, clue consistency, and Sudoku row/column/box validity

The source train/test boundary is preserved. Validation rows come only from
unused upstream training rows. Test and confirmation rows come only from upstream
test files. Full file hashes, row provenance, exclusions, and pairwise overlap
checks are in `metadata/manifest.json` and `metadata/audit.json`.

The upstream Sudoku Extreme corpus states that its official train and test sets
are mathematically inequivalent. This release additionally audits exact and digit
renaming overlap across the mixed original/Extreme sources. It does not implement
full canonicalization over every Sudoku row, column, band, stack, and transpose
symmetry; that remains a documented limitation.

## Schema

The main columns are `puzzle`, `solution`, `difficulty_bucket`,
`difficulty_subbucket`, `official_rating`, `clues`, `source_collection`, and
`release_split`. `example_id`, `puzzle_id`, and `digit_normalized_id` are stable
SHA-256 identifiers. `source_file` and `source_row_index` provide exact provenance.

```python
from datasets import load_dataset

dataset = load_dataset("stwistzz/Sudoku_DLM_Reasoning")
train = dataset["train"]
validation = dataset["validation"]
test = dataset["test"]
```

Puzzles and solutions are 81-character row-major strings. `0` denotes an empty
cell in `puzzle`.

## Rebuild

The pinned source CSV files are included under `raw/`. Run:

```bash
python scripts/build_dataset.py \
  --source-dir raw \
  --output-dir /path/to/release \
  --dataset-id stwistzz/Sudoku_DLM_Reasoning \
  --seed 0
```

The builder requires Python 3.10+ and PyArrow. See `docs/BUILDING.md`, then run
`scripts/verify_dataset.py` against the rebuilt release.

## Attribution and license

The data is derived from
[`fhyfhy/diffusion-vs-ar-hard-sudoku`](https://huggingface.co/datasets/fhyfhy/diffusion-vs-ar-hard-sudoku),
which packages the original Sudoku data from
[`HKUNLP/diffusion-vs-ar`](https://github.com/HKUNLP/diffusion-vs-ar) and the
[`sapientinc/sudoku-extreme`](https://huggingface.co/datasets/sapientinc/sudoku-extreme)
corpus. Please consult and comply with the licenses and attribution requirements
of all upstream sources. This derived release does not grant rights beyond them.
See `RAW_DATA_LICENSES.md` for the raw-snapshot notice.