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rStar-Reasoning/usaco_2025

USACO 2025 Open Contest Dataset Dataset Description The USA Computing Olympiad (USACO) is a prestigious algorithmic programming competition for high school students in the United States, consisting of four difficulty levels: Bronze, Silver, Gold, and Platinum. Each level contains a set of challenging problems that test algorithmic thinking and implementation skills, making USACO a valuable benchmark for evaluating the reasoning and problem-solving capabilities of… See the full description on the dataset page: https://huggingface.co/datasets/rStar-Reasoning/usaco_2025.

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USACO 2025 Open Contest Dataset

Dataset Description

The USA Computing Olympiad (USACO) is a prestigious algorithmic programming competition for high school students in the United States, consisting of four difficulty levels: Bronze, Silver, Gold, and Platinum. Each level contains a set of challenging problems that test algorithmic thinking and implementation skills, making USACO a valuable benchmark for evaluating the reasoning and problem-solving capabilities of large language models. This dataset contains problems and test cases from the USACO 2025 Open Contest.

Data Fields

Each problem in the parquet file contains:

  • —name: Contest and difficulty level
  • —problem_link: Official USACO problem link
  • —test_data_link: Link to test data
  • —solution_link: Link to official solution
  • —problem_level: Difficulty level (bronze/silver/gold/platinum)
  • —description: Full problem statement
  • —input_format: Input specification
  • —output_format: Output specification
  • —samples: Sample test cases included in problem description
  • —runtime_limit: Time limit in seconds
  • —memory_limit: Memory limit in MB
  • —solution: Official solution with explanation

Source Data

Data was collected from the official USACO contest platform https://usaco.org//index.php?page=open25results, copyright not specified.

Citation

If you use this dataset, please cite:

bibtex
@misc{liu2025rstarcoderscalingcompetitivecode,
      title={rStar-Coder: Scaling Competitive Code Reasoning with a Large-Scale Verified Dataset}, 
      author={Yifei Liu and Li Lyna Zhang and Yi Zhu and Bingcheng Dong and Xudong Zhou and Ning Shang and Fan Yang and Mao Yang},
      year={2025},
      eprint={2505.21297},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2505.21297}, 
}