LeetCodeDataset: A Temporal Dataset for Robust Evaluation and Efficient Training of Code LLMs

Yunhui Xia, Wei Shen, Yan Wang, Jason Klein Liu, Huifeng Sun, Siyue Wu, Jian Hu, Xiaolong Xu

LeetCodeDataset: A Temporal Dataset for Robust Evaluation and Efficient Training of Code LLMs: 21 upvotes on Hugging Face Daily Papers, #13 of 28 papers on 2025-04-22. Day-by-day upvote history.

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 page on Hugging Face · arXiv

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