CodeContests, developed by Google DeepMind, is a large-scale competitive programming dataset designed for training and evaluating machine learning models on code generation and problem solving. This dataset played a central role in the development of AlphaCode, DeepMind’s model for solving programming problems at a human-competitive level, as published in Science. CodeContests aggregates problems and human-written solutions from multiple programming competition platforms, including AtCoder, Codeforces, CodeChef, Aizu, and HackerEarth. Each problem includes structured metadata, problem descriptions, paired input/output test cases, and multiple correct and incorrect solutions in various programming languages. The dataset is distributed in Riegeli format using Protocol Buffers, with separate training, validation, and test splits for reproducible machine learning experiments.
Features
- Comprehensive dataset of competitive programming problems and solutions
- Sourced from multiple online judges such as Codeforces, AtCoder, and CodeChef
- Includes both correct and incorrect human solutions with test cases
- Provided in Riegeli format with Protocol Buffer definitions
- Tools for evaluating and executing code submissions in sandboxed environments
- Used in training AlphaCode for program synthesis and competition-level reasoning