Datasets:
task_id stringlengths 8 8 | train listlengths 2 10 | test listlengths 1 4 |
|---|---|---|
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End of preview. Expand in Data Studio
ARC-AGI-2 Grids — training + analysis corpus (NVARC-compatible)
Companion dataset for the Kaggle ARC Prize 2026 (ARC-AGI-2) solver built on
sorokin/qwen3_4b_grids15_sft139 + per-task rank-256 LoRA (NVARC lineage).
Everything here is generated from public canonical data only (1,000
training / 120 evaluation tasks); no hidden competition data is included.
Contents
| Path | Rows | Description |
|---|---|---|
train/train_tasks.jsonl |
1,000 | canonical training tasks (full I/O) |
train/train_solutions.jsonl |
1,000 | public answers (offline scoring ONLY) |
evaluation/evaluation_tasks.jsonl |
120 | canonical evaluation tasks |
evaluation/evaluation_solutions.jsonl |
120 | public answers (offline scoring ONLY) |
sft/train_sft128.jsonl.gz |
128,000 | 128 augmented NVARC transcripts per task (8 dihedral × 16 color perms), chat template, completion field |
features/task_features.jsonl |
1,120 | runtime-safe per-task features (sizes, palettes, shape preservation, context length, difficulty bucket) |
folds/folds.json |
5 folds | deterministic task-grouped folds for OOF selector work |
synthetic/curriculum_v0_verified.jsonl |
410 | program-generated tasks across 7 concept families, each independently re-verified: rule re-fit from demos reproduces every test output |
augmentation_manifest.json |
— | augmentation spec + inversion rules |
Grid format (NVARC-exact)
[[0,1],[2,3]] -> "01\n23"
Chat template per sequence:
<|im_start|>user\n{grid}<|im_end|><|im_start|>assistant\n{grid}<|im_end|> (repeated demos, then query + reply)
Loss is completion-only (assistant replies). Augmentation inversion:
argsort(color_perm) then inverse dihedral — see augment.invert_grid in
the companion repo Nabidnur/arc-agi-2-solver-qwen3-4b.
Scoring contract (documented contest metric)
Output-level exact pass@2:
S = 100 * sum_t sum_j 1[y(t,j) in {attempt_1, attempt_2}] / sum_t m(t)
On the 172-output public evaluation set one extra correct output = +0.5814 points. Milestones (exact-output thresholds): 30%→52, 40%→69, 50%→86, 60%→104.
Data-use boundaries
*_solutions.jsonland synthetic test outputs are for offline development scoring only — never visible to the submission solver.task_featuresare runtime-safe: computed from demos + test inputs only.- Synthetic tasks carry
ancestryrecords (family, params, seed, generator version) — split by ancestry, never by grid.
Provenance
- Canonical tasks: ARC-AGI-2 public release (Kaggle
arc-prize-2026-arc-agi-2). - Six evaluation tasks differ between the Kaggle and GitHub archives
(
4a21e3da,abc82100,b6f77b65,d8e07eb2,f560132c,faa9f03d); this repo uses the Kaggle variant as primary. - Synthetic generator:
datasets_build/gen_synthetic.pyv0.1 + independent verifierdatasets_build/verify_synthetic.py(410/420 kept).
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