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End of preview. Expand in Data Studio
ARC Prize 2026 training data
The training data behind our ARC Prize 2026 solution: the task pools, ARC-AGI-3 RL seeds, the QAD student-trajectory set, the
drafter corpora and the drafter data plans. The code that produced and read them, and the step-by-step lineage of the four final
models, are in github.com/ycchen-tw/ARC-Forge-2026:
training/README.md and
training/TRAINING_MAP.json. Step ids below
(A1, B2, ...) are the steps of TRAINING_MAP.json. Code paths are relative to that repository.
Contents
| folder | contents | format | size | files | step |
|---|---|---|---|---|---|
arc2_pools/ |
ARC-AGI-2 task pools, arc2_eval120fix, selection / order / held-out lists, licence texts (table below) |
ARC task JSON, txt / json lists | 78.1 MB | 2,572 | A1 / B2 collection; C1–C2 (truehard558, ctx64k90v2, arc2_eval120fix) |
arc3_seeds_v8/ |
ARC-AGI-3 RL v8 seed set: 237 level-seed specs (235 train on 18 games, 2 held-out on s5i5), the 237 seed workspaces, build lists | spec JSON; workspaces as one .tar.zst per game (19 archives; 7.08 GB / 477,625 files unpacked) |
1.36 GB | 410 | D2–D3 |
qadmix-1019-1022/ |
QAD R distillation set: student trajectories of the INT3 model. Train 20,200 segments / 1,150,451,017 tokens / 722,931,963 supervised; validation 374 / 20,999,860 / 14,400,806 | tito-mm-v1 in qadmix-1019-1022.tar.zst (20.55 GB / 49,152 files unpacked) + a copy of its manifest.json |
1.09 GB | 2 | B2–B5 |
corpora/nemotron-raw-tokens/ |
Qwen3.8-Flash-Next-FP8 generations on Nemotron-Post-Training-Dataset-v2 prompts, 3 batches: 300,010 rows (59,553 / 200,004 / 40,453), 1.225 B tokens, 1.138 B completion | parquet | 1.95 GB | 9,383 | F2; E2–E3 (D-line plan 20261012-int3-plan) |
corpora/nvarc-teacher-rollouts/ |
Qwen3.8-Flash-Next-FP8 teacher rollouts on NVARC puzzles (CoT : TIR = 1 : 3): 47,335 rows, 1.260 B tokens, 0.900 B completion | parquet | 849 MB | 474 | F3; E2–E3 |
corpora/nvarc-agentic-segments/ |
agentic-harness segments on all 103,223 NVARC puzzles: 125,711 rows, 3.601 B tokens, 2.524 B completion | parquet | 3.73 GB | 92 | F4; E2–E3 |
dflash_plans/ |
drafter capture plans 20261017-codex1015-plan (12,119 jobs) and 20261020-l8-plan (44,123 jobs) |
JSONL + JSON | 21.1 MB | 6 | E2 (built); E3–E4 (codex1015), E5 (l8) |
FILES.tsv lists path, bytes and sha256 of every file in this repository.
arc2_pools/
| entry | tasks | origin | step |
|---|---|---|---|
independent1000/ |
1,000 | our LLM-generated synthetic tasks (work/seed_bank/independent1000) |
A1, B2 |
synth108/ |
108 | our synthetic families (work/seed_bank/synth) |
A1, B2 |
quality300/ |
300 | our quality-filtered synthetic tasks, at least two parents each (work/seed_bank/quality300) |
B2 |
largegrid30-20261022/ |
378 | ARC-AGI-2 training families regenerated at 30×30 with google/ARC-GEN @a15cbdb4 (tasks/, manifest.jsonl, receipt.json) |
B2 |
truehard558-20261028/ + 5 lists |
558 | our synthetic tasks that QAD R failed or solved with difficulty in the 2026-10-27 solvability check: independent1000 403, quality200 54, quality300 37, compositions105 33, size156 18, restricted116 9, synth108 4 | C1 |
ctx64k90v2-20261029/ + .selection.json, .train.txt, .untrained_eval120fix.txt |
90 | 60 arc2_eval120fix tasks + 30 audited truehard558 tasks, seed 20261029; the RL r4 pool |
C1–C2 |
arc2_eval120fix/ (evaluation/, MANIFEST.json) |
120 | ARC-AGI-2 public evaluation with 6 tasks corrected (below) | C1 |
nvarc_training.order.txt, nvarc_full.order.txt |
47,337 / 55,886 ids | our collection order over NVARC puzzle ids; the puzzles are not included | A1, B2 |
licenses/ |
ARC-AGI-2, ARC-ATLAS, ARC-GEN (Apache-2.0) and re-arc (MIT) licence texts |
Format
- ARC task JSON (
arc2_pools/**/<task_id>.json):{"train": [{"input", "output"}, ...], "test": [...]}, grids are lists of rows of integers 0–9.ctx64k90v2-20261029is built bytraining/work/qwen38fn_rl/probes/arc2_ctx64k_20261029/build_pool.py,arc2_eval120fixbytraining/work/arc_code/arc2/eval120fix/build.pyfromfixes.json; itsMANIFEST.jsonrecords the upstream and the fixed sha256 of every task. - Seed specs (
arc3_seeds_v8/g2seed-v8-20261029/<id>.json):kind: g2seedstubs fortraining/work/arc_code/run_arc3_level.py --tasks-dir(harness kindarc3_g2seed);seedis aSeedSpecoftraining/src/arcforge/arc_code/level_seed.py. Unpack insidearc3_seeds_v8/(for f in seed_archives/*.tar.zst; do tar --zstd -xf "$f"; done);seed.wsis relative toarc3_seeds_v8/.seed.sha256is thelevel_seed.tree_sha256of the released workspace and is checked at load.seed.prefixis the replay-workspace path string inside the seed files, whichlevel_seed.loadreplaces with the new workspace path.seeds_manifest.jsonlgives per seed the released and the original (sha256_source) tree sha256, files, bytes and the files whose prefix string was rewritten.lists/holdstrain.txt,heldout.txt,classes.jsonand the build inputs oftraining/work/qwen38fn_rl/probes/arc3v8_20261029/seeds/build_v8_dir.py. - tito-mm-v1 (
qadmix-1019-1022.tar.zst): per splitinput_ids.i32,loss_mask.u8,mrope.i32(3 × int32 per token), flat little-endian arrays, andindex.jsonl(one row per segment: key, offset, length, media);media.jsonlandmedia/<2 hex>/<sha256>.npz(pixel frames per unique image or video). Reader:training/work/qwen38fn_lora/tito_mm_data.py(TitoMMDataset). Built bytraining/work/qwen38fn_qad/probes/student_traj_1022/filter/merge_filter.py, trained on bytraining/work/qwen38fn_qad/train_qad.py. The B4 teacher targets (139 GB) are not included. - Tokenizer: all token ids index the Qwen3.8-Flash-Next tokenizer (not included),
tokenizer.jsonsha2560997f410c57a1f4e53b09e4be8f4a172d90edd9564368fb0847030937229b9f3(from the qadmixmanifest.json). - Parquet corpora: one token sequence per row.
nemotron-raw-tokens:input_ids,loss_mask(prompt 0, generation 1),n_prompt,n_completion,effort,finish_reason,split,meta_info(server response metadata),provenance(the source sample's category, generator, licence and uuid),uid.nvarc-teacher-rollouts,nvarc-agentic-segments: flatidswithspan_start/span_end/span_role, the sampling setup and outcome columns; the agentic set addsepisode,segment_idx,ctx_cap,task_correct,source(nvarc_training/nvarc_full) andrun. Reader:training/work/qwen38fn_dflash/data_plan.py; producerstraining/work/qwen38fn_specdata/generate.py,training/work/qwen38fn_distill/,training/work/agentic_distill/render_segments.py. - Drafter plans:
plan.jsonlhas one capture job per line (key,split,corpus,subset,group,ref.instance/ref.seginto a TITO ledger,num_tokens,supervised,eligible_anchors,ids_sha256);plan_meta.jsonholds the argv, sources and held-out rule,summary.jsonthe totals. Built bytraining/work/qwen38fn_dflash/make_data_plan.py, read bydata_plan.pyandcapture_plan.py. The ledgers they index are not included.
Machine paths and host names in metadata were rewritten with the convention of training/run_records: $ARC_RUNS,
$ARC_DATA, $ARC_MODELS, $ARC_RAM_RUNS, $ARC_NFS, $HOME, uid-$UID, and node1 / node2 for hosts and in instance ids.
Seed prefix strings use /ARC_RUNS/..., because SeedSpec requires an absolute path. Token ids, labels, grids, arrays and media
are unchanged. Digests recorded inside rewritten metadata (plan_sha256, processor_digest, the seeds' sha256_source) were
computed before the rewrite.
Evaluation overlap
ctx64k90v2-20261029contains 60 of the 120 ARC-AGI-2 public evaluation tasks; RL r4 (C2) trained on them. The other 60 are listed inctx64k90v2-20261029.untrained_eval120fix.txt.independent1000,synth108andquality300tasks have ARC-AGI-2 evaluation tasks as parents; NVARCnvarc_fullfamilies are named after evaluation tasks;truehard558is drawn from our synthetic sets (403 tasks fromindependent1000). In qadmix these sources are 20.6% of train supervised tokens (quality300 10.2%, independent1000 6.1%, nvarc_full 3.4%, synth108 0.9%).- The ARC-AGI-3 seeds come from 19 of the 25 public ARC-AGI-3 games.
Licenses and sources
- Our own generated content: CC0-1.0. The synthetic pools (
independent1000,synth108,quality300,truehard558), the selection / order / held-out lists, the seed specs and seed workspaces, the drafter plans, and the eval120fix corrections. - Model-generated text. The qadmix trajectories, the three corpora and the files the model wrote in the seed workspaces were generated by Qwen3.8-Flash-Next or by our derivatives of it (expert-pruned, INT3-quantized, distilled). The Qwen Community License 1.0 may apply to model outputs.
- Third-party-derived parts. They keep their original terms; users must check those terms before use.
- ARC-AGI-2 (arcprize/ARC-AGI-2 @f3283f7, Apache-2.0):
arc2_eval120fixis the public evaluation set with 6 tasks / 29 cells corrected: train-pair errors in 581f7754 (8 cells), e12f9a14 (6), b6f77b65 (1), a6f40cea (8); test-side defects, input and output changed together, in 1ae2feb7 (2) and 53fb4810 (4). The qadmix prompts also contain ARC-AGI-2 training tasks. - quality300 / independent1000: parents from ARC-AGI-2, ARC-GEN and ARC-ATLAS (Apache-2.0) and re-arc (MIT); licence texts
in
arc2_pools/licenses/.largegrid30-20261022was generated with google/ARC-GEN (Apache-2.0). - NVARC puzzles: Kaggle
sorokin/nvarc-synthetic-puzzles, licence field "Unknown", no LICENSE in the archive. Used innvarc-teacher-rollouts(nvarc_training),nvarc-agentic-segments(nvarc_training + nvarc_full) and 35.5% of qadmix train supervised tokens (nvarc_training 32.1%, nvarc_full 3.4%). The puzzles appear inside prompts; the puzzle files are not included. - ARC-AGI-3 community games: 42.2% of qadmix train supervised tokens are trajectories, with rendered frames, on community-made ARC-AGI-3 games, collected from 23 sources. Their licences are recorded per source in our corpus catalog: 1,003 of the 1,281 games used have no declared licence or are marked "private research only". The games themselves are not redistributed. The drafter plans index trajectories on the same games (ids only).
- ARC-AGI-3 public games (ARC Prize Foundation): the seed workspaces contain recorded frames (
record/) of the public games they were played on (19 of the 25); no game code. - Nemotron-Post-Training-Dataset-v2 (NVIDIA): source of the
nemotron-raw-tokensprompts. CC-BY-4.0 with per-sample exceptions (a Wildchat subset under ODC-BY, a StackOverflow subset under CC-BY-SA), recorded in each row'sprovenance. Only the token ids of the prompts and our model's outputs are stored, not the dataset's responses.
- ARC-AGI-2 (arcprize/ARC-AGI-2 @f3283f7, Apache-2.0):
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