README.md

metadata

dataset_info:
  features:
    - name: year
      dtype: string
    - name: id
      dtype: string
    - name: problem
      dtype: string
    - name: solution
      dtype: string
    - name: answer_type
      dtype: string
    - name: source
      dtype: string
    - name: type
      dtype: string
    - name: original_problem
      dtype: string
    - name: original_solution
      dtype: string
    - name: variation
      dtype: int64
  splits:
    - name: train
      num_bytes: 420734.1954022989
      num_examples: 150
    - name: validation
      num_bytes: 420734.1954022989
      num_examples: 150
    - name: test
      num_bytes: 622686.6091954022
      num_examples: 222
  download_size: 822829
  dataset_size: 1464155
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: validation
        path: data/validation-*
      - split: test
        path: data/test-*

Putnam-AXIOM Splits for ZIP-FIT

This repository contains the train, validation, and test splits of the Putnam-AXIOM dataset specifically for use with the ZIP-FIT methodology research. The dataset is split as follows:

• train: 150 examples
• validation: 150 examples
• test: 222 examples

These splits are derived from the original 522 Putnam problems found in the main Putnam-AXIOM repository.

Main Repository

The full dataset with original problems and variations is available in the main repository: Putnam-AXIOM/putnam-axiom-dataset-ICML-2025-522

Usage

from datasets import load_dataset

# Load the dataset
dataset = load_dataset("zipfit/Putnam-AXIOM-for-zip-fit-splits")

# Access each split
train_data = dataset["train"]
validation_data = dataset["validation"]
test_data = dataset["test"]

Citation

If you use this dataset, please cite it as follows:

@article{putnam_axiom2025,
  title={Putnam-AXIOM: A Functional and Static Benchmark for Measuring Higher Level Mathematical Reasoning},
  author={Aryan Gulati and Brando Miranda and Eric Chen and Emily Xia and Kai Fronsdal and Bruno de Moraes Dumont and Sanmi Koyejo},
  journal={39th International Conference on Machine Learning (ICML 2025)},
  year={2025},
  note={Preprint available at: https://openreview.net/pdf?id=YXnwlZe0yf, ICML paper: https://openreview.net/forum?id=kqj2Cn3Sxr}
}

@article{miranda2024zipfit,
  title={ZIP-FIT: Embedding-Free Data Selection via Compression-Based Alignment},
  year = {2024},
  journal = {arXiv preprint arXiv:2410.18194},
}

ZIP-FIT Project

For more information on the ZIP-FIT framework, including code and documentation, visit the ZIP-FIT GitHub repository.

License

This dataset is licensed under the Apache 2.0.