LeetCodeDataset: A Temporal Dataset for Robust Evaluation and Efficient
  Training of Code LLMs
Authors: name: Yunhui Xia, name: Wei Shen, name: Yan Wang, name: Jason Klein Liu, name: Huifeng Sun, name: Siyue Wu, name: Jian Hu, name: Xiaolong Xu

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.