| --- |
| license: mit |
| task_categories: |
| - text-generation |
| tags: |
| - arc-agi |
| - re-arc |
| - test-time-training |
| - synthetic-data |
| pretty_name: ARC Tiny Transformer corpus and evaluation candidates |
| --- |
| |
| # ARC Tiny Transformer data |
|
|
| Data and vote-level artifacts for [N8python/arc-tiny-transformer](https://github.com/N8python/arc-tiny-transformer). |
|
|
| ## Corpus |
|
|
| `corpus/documents_3k_per_task.jsonl.zst` is the exact pretraining corpus: |
|
|
| - 1,200,000 JSONL documents; |
| - 3,000 documents for each of 400 RE-ARC generator families; |
| - 3,401,127,124 ARC tokens; |
| - 10,413,648,837 uncompressed bytes; |
| - uncompressed SHA-256 `5d1336f8d6f45358a377af2e3fd05d43c4bfe91621b0c6a44c53dad0e34ca430`. |
|
|
| Each record contains token IDs and metadata for task ID, sampled difficulty band, demonstration count, rejection statistics, and generation seed. The fixed vocabulary and sampler are in the GitHub repository. `manifests/` contains the complete task-level corpus provenance. |
|
|
| ## Evaluation artifacts |
|
|
| `artifacts/` contains the full 128-candidate records for: |
|
|
| - the frozen 50M model; |
| - TTT replicas seeded 57, 58, and 59; |
| - the 7M TTT run. |
|
|
| These files are sufficient to recompute rank-1, raw-frequency top-2, historical hierarchical top-2, oracle accuracy, candidate-scaling curves, and multi-replica ensembles without rerunning model inference. |
|
|
| `viewer/data/` contains the compact per-query data backing the browser-based 384-candidate explorer. |
|
|
| ## Licensing note |
|
|
| Release code and metadata are MIT licensed. The corpus is procedurally generated with the pinned [RE-ARC](https://github.com/michaelhodel/re-arc) generators/verifiers; ARC-AGI task files are not duplicated here. Users should also review the upstream RE-ARC and ARC-AGI licenses for their use case. |
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