{"url":"/dataset/codecontests","name":"CodeContests","full_name":null,"description_markdown":"CodeContests is a competitive programming dataset for machine-learning. This dataset was used when training [AlphaCode](https://deepmind.com/blog/article/Competitive-programming-with-AlphaCode).\r\n\r\nIt consists of programming problems, from a variety of sources.\r\n\r\nProblems include test cases in the form of paired inputs and outputs, as well as both correct and incorrect human solutions in a variety of languages.","description_withheld":null,"homepage":"https://github.com/deepmind/code_contests/","introduced_date":"2022-02-02","introduced_date_note":null,"introduced_by":null,"license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"modalities":[{"name":"Texts","url":"/datasets/modality/texts"}],"tasks":[{"name":"Code Generation","url":"/task/code-generation","datasets_with_task":"/datasets/task/code-generation"},{"name":"Text-to-Code Generation","url":"/task/text-to-code-generation","datasets_with_task":"/datasets/task/text-to-code-generation"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["CodeContests"],"data_loaders":[{"repo":"https://github.com/huggingface/datasets","url":"https://huggingface.co/datasets/deepmind/code_contests","frameworks":["tf","pytorch","jax"]}],"num_papers_in_archive":84,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/code-generation-on-codecontests","task":"Code Generation","dataset_variant":"CodeContests","rows":8,"metrics":["Test Set pass@1","Test Set pass@5","Val Set pass@1","Val Set pass@5"],"first_row_in_archive_order":{"model":"EG-CFG (DeepSeek-V3-0324)","paper":"/paper/execution-guided-line-by-line-code-generation","metrics":{"Test Set pass@1":"58.18"},"code_links":[{"title":"boazlavon/eg_cfg","url":"https://github.com/boazlavon/eg_cfg"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/execution-guided-line-by-line-code-generation","title":"Execution Guided Line-by-Line Code Generation","date":"2025-06-12","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/codesim-multi-agent-code-generation-and-1","title":"CODESIM: Multi-Agent Code Generation and Problem Solving through Simulation-Driven Planning and Debugging","date":"2025-02-08","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-25T09:33:49+00:00","samples_harvested":3,"samples_ran":3,"samples_unverified":0,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/planning-driven-programming-a-large-language","title":"Planning-Driven Programming: A Large Language Model Programming Workflow","date":"2024-11-21","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-25T09:33:49+00:00","samples_harvested":9,"samples_ran":0,"samples_unverified":9,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/mapcoder-multi-agent-code-generation-for","title":"MapCoder: Multi-Agent Code Generation for Competitive Problem Solving","date":"2024-05-18","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-25T09:33:49+00:00","samples_harvested":10,"samples_ran":6,"samples_unverified":4,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/motcoder-elevating-large-language-models-with","title":"MoTCoder: Elevating Large Language Models with Modular of Thought for Challenging Programming Tasks","date":"2023-12-26","rows_on_this_dataset":2,"code_links":1,"syntology":{"read_at":"2026-09-25T09:33:49+00:00","samples_harvested":8,"samples_ran":4,"samples_unverified":4,"pointer_only_for_licence":8,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/codechain-towards-modular-code-generation","title":"CodeChain: Towards Modular Code Generation Through Chain of Self-revisions with Representative Sub-modules","date":"2023-10-13","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-25T09:33:49+00:00","samples_harvested":3,"samples_ran":3,"samples_unverified":0,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/wizardcoder-empowering-code-large-language","title":"WizardCoder: Empowering Code Large Language Models with Evol-Instruct","date":"2023-06-14","rows_on_this_dataset":1,"code_links":4,"syntology":{"read_at":"2026-09-25T09:33:49+00:00","samples_harvested":7,"samples_ran":6,"samples_unverified":1,"pointer_only_for_licence":1,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}}],"syntology_totals":{"read_at":"2026-09-25T09:33:49+00:00","papers_with_samples":6,"samples_harvested":40,"samples_ran":22,"samples_unverified":18,"pointer_only_for_licence":9,"papers_with_no_sample_that_ran":1,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}