nvidia/Nemotron-RL-ARC-AGI-v1

Dataset Description:

Nemotron-RL-ARC-AGI-v1 is a reinforcement-learning (RL) gym environment dataset of single-step ARC-AGI puzzle prompts intended for RL post-training of large language models. Each row corresponds to one ARC puzzle (a set of (input grid, output grid) demonstration pairs plus a single test input grid) rendered as a text prompt; reward is binary (1.0 / 0.0) determined by exact-match comparison against the ground-truth output grid. No LLM judge is used, no partial credit is given, and verification is fully deterministic.

The dataset ships two task variants, released as Hugging Face subsets so they can be selected independently:

• transductive — the model must output the solution grid directly in \boxed{} format; the parsed grid is exact-matched against expected_output.
• python_inductive — the model must output a Python transform(grid) function that is executed (30-second timeout) on the test input; the resulting grid is exact-matched against expected_output.

Both variants are single-step (one model response per problem). The same 10,000 training problems and 514 validation problems are used across both variants; only the rendered prompt and the reward extraction logic differ. The 514 validation problems are the union of the ARC-AGI-1 evaluation set (400 problems) and the ARC-AGI-2 evaluation set (120 problems, deduplicated), which are blacklisted from training to prevent leakage.

Seed puzzles come from the same three publicly available sources used by the companion SFT dataset: ARC-AGI-2 (Apache-2.0), the NVARC Augmented Puzzles set published by the NVIDIA Kaggle team, and the arc-dataset-collection (20 community-collected subsets, predominantly under MIT or Apache-2.0 licenses). 10,000 problems are sampled uniformly at random from the ~66K eligible problems (filtered to ≤5 training examples per problem and excluding all validation problem IDs). No augmentations are released — one row per problem.

The released dataset contains text prompts derived from publicly available ARC puzzle data, ground-truth grids for verification, and per-problem lineage metadata (difficulty, source dataset, augmentation parameters). No original puzzle JSON files and no model weights are redistributed. The companion verification code is delivered separately as a NeMo Gym resource server.

This dataset is for research and development only.

Dataset Owner(s):

NVIDIA Corporation

Dataset Creation Date:

Created on: 2026-03-31 Last Modified on: 2026-06-02

Version

Nemotron-RL-ARC-AGI-v1

License/Terms of Use:

This dataset is licensed under CC BY 4.0. Additional info: Apache 2.0 and MIT License.

Intended Usage:

This dataset is intended for researchers and model developers performing reinforcement-learning post-training on visual/abstract reasoning. Typical use cases include: RLHF/RLAIF on agentic reasoning models, reward-model-free RL with deterministic verification, difficulty-curriculum experiments (each row carries difficulty and difficulty_bucket), and reward-profiling to select hard subsets for focused RL passes. The dataset is text-only; ARC grids are presented as formatted text descriptions rather than images.

Dataset Characterization

Data Collection Method

• Automated: ARC puzzle seed data is collected programmatically from the three public sources listed above; sampling and prompt rendering are deterministic.

Labeling Method

• Automated: reward labels (1.0 / 0.0) are produced at training time by a deterministic exact-match comparison between the model's submitted grid and the ground-truth solution shipped with each puzzle. No human or LLM labeling is involved.

Dataset Format

Modality: Text Format: JSONL Structure: Each row is one ARC puzzle prompt with top-level fields:

• responses_create_params.input — OpenAI-format [{role: system, content: ...}, {role: user, content: ...}] rendered prompt for the variant.
• expected_output — 2D integer array (the ground-truth output grid).
• train — list of {input, output} demonstration pairs (input + output grids).
• test_input — 2D integer array (the test input grid).
• problem_id, task_id — lineage identifiers.
• variant — "transductive" or "python_inductive".
• difficulty (float ∈ [0, 1], higher = harder), difficulty_bucket ("easy" / "medium" / "hard").
• augmentation — {augmentation_index, is_augmented, d4_index, color_permutation, train_shuffle}. All released rows are unaugmented (is_augmented: false).
• original_problem, metadata, agent_ref — additional lineage / source information.

Reward / verification (applied at training time, not embedded in the rows):

• transductive: parse grid from \boxed{...} in model response → exact match against expected_output → reward ∈ {0, 1}.
• python_inductive: extract Python code from response → execute with 30-second timeout → exact match against expected_output → reward ∈ {0, 1}.

Dataset Quantification

Subset	Split	Rows	Unique problems	Disk
transductive	train	10,000	10,000	272 MB
transductive	validation	514	514	8.4 MB
python_inductive	train	10,000	10,000	297 MB
python_inductive	validation	514	514	9.5 MB
Total		21,028		~587 MB

The 10K training problems are sampled uniformly at random from ~66K eligible problems (≤5 training examples per problem, with all 514 validation problem IDs blacklisted). Difficulty breakdown of the 10K training set (difficulty_bucket):

Difficulty	Count	%
Easy (< 0.3)	1,201	12.0%
Medium (0.3–0.7)	3,591	35.9%
Hard (≥ 0.7)	5,208	52.1%
Total	10,000

Validation set composition: ARC-AGI-1 evaluation (400 problems) + ARC-AGI-2 evaluation (120 problems, with 6 shared with ARC-AGI-1 deduplicated) = 514 unique problem IDs.

The full eligible pool of ~73K unique problems remains available for augmentation (D4 symmetry transforms + color permutations + train-example shuffling, up to 8× per problem) if downstream training requires scaling the dataset.

Reference(s):

Seed datasets:

• ARC-AGI-2 — https://github.com/arcprize/ARC-AGI-2
• NVARC Augmented Puzzles — https://www.kaggle.com/datasets/sorokin/nvarc-augmented-puzzles
• arc-dataset-collection — https://github.com/neoneye/arc-dataset-collection

Companion SFT release (same problem pool, different post-training stage):

• Nemotron-SFT-ARC-AGI-v1 — https://huggingface.co/datasets/nvidia/Nemotron-SFT-ARC-AGI-v1

Upstream construction issue: https://gitlab-master.nvidia.com/fsoares/post-training-data-processing/-/issues/103

Ethical Considerations:

NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal developer teams to ensure this dataset meets requirements for the relevant industry and use case and addresses unforeseen product misuse. Please report quality, risk, security vulnerabilities or NVIDIA AI Concerns here