title: LeetCodeDataset: A Temporal Dataset for Robust Evaluation and Efficient
    Training of Code LLMs
summary: We introduce LeetCodeDataset, a high-quality benchmark for evaluating and
  training code-generation models, addressing two key challenges in LLM research:
  the lack of reasoning-focused coding benchmarks and self-contained training
  testbeds. By curating LeetCode Python problems with rich metadata, broad
  coverage, 100+ test cases per problem, and temporal splits (pre/post July
  2024), our dataset enables contamination-free evaluation and efficient
  supervised fine-tuning (SFT). Experiments show reasoning models significantly
  outperform non-reasoning counterparts, while SFT with only 2.6K model-generated
  solutions achieves performance comparable to 110K-sample counterparts. The
  dataset and evaluation framework are available on Hugging Face and Github.
paper_authors._id: 68070991b0cb768cae20c585
paper_authors.name: Yunhui Xia
paper_authors._id: 68070991b0cb768cae20c586
paper_authors.name: Wei Shen
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-22T10:59:51.173Z
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paper_authors.avatarUrl: /avatars/70cb7d65d30ecb944595000ceeeedb1b.svg
paper_authors.fullname: Wei Shen
paper_authors.type: user
paper_authors.user: Swtheking
paper_authors._id: 68070991b0cb768cae20c587
paper_authors.name: Yan Wang
paper_authors._id: 68070991b0cb768cae20c588
paper_authors.name: Jason Klein Liu
paper_authors.status: extracted_pending
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paper_authors.fullname: Jason Klein Liu
paper_authors.type: user
paper_authors.user: jasonkleinlove
paper_authors._id: 68070991b0cb768cae20c589
paper_authors.name: Huifeng Sun
paper_authors._id: 68070991b0cb768cae20c58a
paper_authors.name: Siyue Wu
paper_authors._id: 68070991b0cb768cae20c58b
paper_authors.name: Jian Hu
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2025-06-05T00:20:55.022Z
paper_authors._id: 63f6c04ac96958470d1e9043
paper_authors.avatarUrl: /avatars/da46cdd9e21498e120ca91b67bfbfb5e.svg
paper_authors.fullname: Jian Hu
paper_authors.type: user
paper_authors.user: chuyi777
paper_authors._id: 68070991b0cb768cae20c58c
paper_authors.name: Xiaolong Xu
paper_title: LeetCodeDataset: A Temporal Dataset for Robust Evaluation and Efficient
    Training of Code LLMs
paper_summary: We introduce LeetCodeDataset, a high-quality benchmark for evaluating and
  training code-generation models, addressing two key challenges in LLM research:
  the lack of reasoning-focused coding benchmarks and self-contained training
  testbeds. By curating LeetCode Python problems with rich metadata, broad
  coverage, 100+ test cases per problem, and temporal splits (pre/post July
  2024), our dataset enables contamination-free evaluation and efficient
  supervised fine-tuning (SFT). Experiments show reasoning models significantly
  outperform non-reasoning counterparts, while SFT with only 2.6K model-generated
  solutions achieves performance comparable to 110K-sample counterparts. The
  dataset and evaluation framework are available on Hugging Face and Github.
paper_ai_summary: LeetCodeDataset provides a benchmark for evaluating and training code-generation models with reasoning-focused coding tasks and supports efficient supervised fine-tuning.
paper_ai_keywords: code-generation models reasoning-focused coding self-contained training testbeds rich metadata broad coverage temporal splits supervised fine-tuning reasoning models model-generated solutions