LeetCodeDataset: A Temporal Dataset for Robust Evaluation and Efficient
  Training of Code LLMs
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.
68070991b0cb768cae20c585
Yunhui Xia
68070991b0cb768cae20c586
Wei Shen
admin_assigned
2025-04-22T10:59:51.173Z
6468823272d9180d4ac90bdf
/avatars/70cb7d65d30ecb944595000ceeeedb1b.svg
Wei Shen
user
Swtheking
68070991b0cb768cae20c587
Yan Wang
68070991b0cb768cae20c588
Jason Klein Liu
extracted_pending
2025-04-22T03:14:26.859Z
6731c04d5f55903a1d8c307c
/avatars/704b1d628c55e3141194b08736f21267.svg
Jason Klein Liu
user
jasonkleinlove
68070991b0cb768cae20c589
Huifeng Sun
68070991b0cb768cae20c58a
Siyue Wu
68070991b0cb768cae20c58b
Jian Hu
extracted_confirmed
2025-06-05T00:20:55.022Z
63f6c04ac96958470d1e9043
/avatars/da46cdd9e21498e120ca91b67bfbfb5e.svg
Jian Hu
user
chuyi777
68070991b0cb768cae20c58c
Xiaolong Xu
LeetCodeDataset: A Temporal Dataset for Robust Evaluation and Efficient
  Training of Code LLMs
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.
LeetCodeDataset provides a benchmark for evaluating and training code-generation models with reasoning-focused coding tasks and supports efficient supervised fine-tuning.
code-generation models reasoning-focused coding self-contained training testbeds rich metadata broad coverage temporal splits supervised fine-tuning reasoning models model-generated solutions