deepscaler
DeepScaleR-Preview-Dataset
Data
Our training dataset consists of approximately 40,000 unique mathematics problem-answer pairs compiled from:
AIME (American Invitational Mathematics Examination) problems (1984-2023)
AMC (American Mathematics Competition) problems (prior to 2023)
Omni-MATH dataset
Still dataset
Format
Each row in the JSON dataset contains:
problem: The mathematical question text, formatted with LaTeX notation.
solution: Offical solution to the problem, including LaTeX formatting… See the full description on the dataset page: https://huggingface.co/datasets/agentica-org/DeepScaleR-Preview-Dataset.deepscaler-preview-verl
DeepScaleR-Preview VERL
📊 Dataset Summary
This dataset contains 35,789 mathematical reasoning problems in VERL format, processed from agentica-org/DeepScaleR-Preview-Dataset.
Key Features:
35,789 high-quality math problems
Converted to VERL format for reward modeling
Verified ground truth answers
Ready for reinforcement learning training
🔗 Source Dataset
Original Repository
Repository:… See the full description on the dataset page: https://huggingface.co/datasets/sungyub/deepscaler-preview-verl.DeepScaleR-Preview-Dataset-verl-formatswift-reasoning-rollouts-deepscaler-ministral8b
DeepScaleR Reasoning Rollouts (Ministral-8B)
This dataset contains reasoning rollouts used to train the SWIFT reward head.
Paper page: https://huggingface.co/papers/2505.12225
GitHub: https://github.com/aster2024/SWIFT/
Generator model: mistralai/Ministral-8B-Instruct-2410 (https://huggingface.co/mistralai/Ministral-8B-Instruct-2410)
Dataset Description
This dataset contains 10000 samples corresponding to the Generalization Test setup.
Source: DeepScaleR.
Generator:… See the full description on the dataset page: https://huggingface.co/datasets/Aster2024/swift-reasoning-rollouts-deepscaler-ministral8b.aime-solution-hint-v6-deepscaler-respgenDeepScaleR-Easy-Medium-Hard-Gemma-26B-PT-10k
DeepScaleR Easy/Medium/Hard — Gemma 4 26B-A4B PT
This dataset contains 9,900 unique, deduplicated DeepScaleR math questions for
reinforcement-learning experiments. Difficulty is defined by how often the
pretrained google/gemma-4-26B-A4B teacher solved each question across eight
temperature-1 samples under the same rule-based grader used by the RL training
pipeline.
The Hub dataset has three configurations—easy, medium, and hard—and each
configuration has a train split with 3,000… See the full description on the dataset page: https://huggingface.co/datasets/JWei05/DeepScaleR-Easy-Medium-Hard-Gemma-26B-PT-10k.