datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
leetcode-problem-solutions
LeetCode Solution Dataset
This dataset contains community-contributed LeetCode solutions scraped from public discussions and solution pages, enriched with metadata such as vote counts, author info, tags, and full code content. The goal is to make high-quality, peer-reviewed coding solutions programmatically accessible for research, analysis, educational use, or developer tooling.
Column Descriptions
Column Name
Type
Description
question_slug
string
The unique… See the full description on the dataset page: https://huggingface.co/datasets/kaysss/leetcode-problem-solutions.deepseek-leetcodeDeepseek Leetcode dataset from https://github.com/deepseek-ai/DeepSeek-Coder/tree/main/Evaluation/LeetCode
leetcode-problem-set
LeetCode Scraper Dataset
This dataset contains information scraped from LeetCode. It is designed to assist developers in analyzing LeetCode problems, generating insights, and building tools for competitive programming or educational purposes.
Dataset Contents
The dataset includes the following files:
problem_set.csv
Contains a list of LeetCode problems with metadata such as difficulty, acceptance rate, tags, and more.
Columns:
acRate: Acceptance rate of the… See the full description on the dataset page: https://huggingface.co/datasets/kaysss/leetcode-problem-set.doocs-leetcode-solutions
Doocs LeetCode Solutions
LeetCode problems with solutions in many programming languages, built from the Doocs LeetCode repository. Each solution comes with the approach name, the reasoning that leads to it, and an explanation with complexity analysis. The dataset is meant for fine-tuning and evaluating code generation models.
The dataset is regenerated monthly from the latest Doocs commit by the leetcode-dataset-generator tool.
Structure
The dataset has two… See the full description on the dataset page: https://huggingface.co/datasets/olegshulyakov/doocs-leetcode-solutions.leetcode-problem-detailed
LeetCode Scraper Dataset
This dataset contains information scraped from LeetCode, including problem details, metadata, and related files. It is designed to assist developers in analyzing LeetCode problems, generating insights, and building tools for competitive programming or educational purposes.
questions_deets.csv
Contains detailed information about each problem, including problem descriptions, constraints, and examples.
Columns:
questionFrontendId: Unique problem ID.… See the full description on the dataset page: https://huggingface.co/datasets/kaysss/leetcode-problem-detailed.grpo-qwen3-1.7b-nemotron-leetcode-clean-3.2k-bs32-n8-verl091-epoch2-146102-rollouts
Coding RL rollouts
grpo_Qwen3-1.7B_Nemotron-LeetCode-clean-3.2k_bs32_n8_seqs16_32k_epoch2_verl091
One verified gzip JSONL shard per training step; 256 responses per shard.
LCB binary grading after thinking, without an EOS gate.
grpo-qwen3-1.7b-nemotron-leetcode-clean-3.2k-bs32-n8-verl091-146102-rollouts
Coding GRPO rollouts
grpo_Qwen3-1.7B_Nemotron-LeetCode-clean-3.2k_bs32_n8_seqs16_32k_1epoch_verl091
One verified gzip JSONL shard per training step; 256 responses per shard.
LCB binary grading after thinking, without an EOS gate.
leetcode-complete
Complete LeetCode Problems Dataset
This dataset contains a comprehensive collection of LeetCode problems (including premium) with AI-generated solutions in JSONL format. It is regularly updated to include new problems as they are added to LeetCode.
Splits
The dataset is divided into the following splits:
train: Contains approximately 80% of the problems for training
validation: Contains approximately 10% of the problems for validation
test: Contains approximately… See the full description on the dataset page: https://huggingface.co/datasets/whiskwhite/leetcode-complete.qrpo-paper-llama-nosft-leetcode-sandbox-temp1-ref50-offpolicy10random-sandbox
qrpo-paper-llama-nosft-leetcode-sandbox-temp1-ref50-offpolicy10random-sandbox
Dataset with reference completions and rewards for a specific model and reward model, ready for training with the QRPO reference codebase (https://github.com/CLAIRE-Labo/quantile-reward-policy-optimization).
Part of the dataset collection for the paper Quantile Reward Policy Optimization: Alignment with Pointwise Regression and Exact Partition Functions (https://arxiv.org/pdf/2507.08068).
leetcode-solutionsFrom: https://www.kaggle.com/datasets/jacobhds/leetcode-solutions-and-content-kpis
qrpo-paper-llama-sft-leetcode-sandbox-temp1-ref50-offpolicy10random-sandbox
qrpo-paper-llama-sft-leetcode-sandbox-temp1-ref50-offpolicy10random-sandbox
Dataset with reference completions and rewards for a specific model and reward model, ready for training with the QRPO reference codebase (https://github.com/CLAIRE-Labo/quantile-reward-policy-optimization).
Part of the dataset collection for the paper Quantile Reward Policy Optimization: Alignment with Pointwise Regression and Exact Partition Functions (https://arxiv.org/pdf/2507.08068).
leetcode-python-solutions-with-exaplanationsleetcode-assembly
LeetCode Assembly Dataset
441 LeetCode problems solved in C, compiled to assembly across 4 architectures, 2 compilers, and 4 optimization levels using GCC and Clang via the Godbolt Compiler Explorer API.
Dataset Summary
Stat
Value
Total rows
14,112
Unique problems
441
Architectures
x86-64, AArch64, MIPS64, RISC-V 64
Compilers
GCC 15.2, Clang 21.1.0
Optimization levels
-O0, -O1, -O2, -O3
Compilation success rate
100%
Difficulty split
Easy: 98… See the full description on the dataset page: https://huggingface.co/datasets/ronantakizawa/leetcode-assembly.leetcode_preference
Dataset Card for LeetCode Preference
Dataset Summary
This dataset facilitates experiments utilizing Direct Preference Optimization (DPO) as outlined in the paper titled Direct Preference Optimization: Your Language Model is Secretly a Reward Model. This repository provides code pairings crafted by CodeLLaMA-7b. For every LeetCode question posed, CodeLLaMA-7b produces two unique solutions. These are subsequently evaluated and ranked by human experts based on their accuracy… See the full description on the dataset page: https://huggingface.co/datasets/minfeng-ai/leetcode_preference.leetcode-standalone-wordsleetcode_with_youtube_captionsleetcode_free_questions_labeledleetcodeThis dataset contains python solutions for various Leetcode problems, scraped from different posts by users from the solutions tab.
leetcode_with_youtube_captionsLeetCodeDataset-qwen3.5-0.8b-rl-bandleetcode-no-depsb2_code_fasttext_pos_leetcode_neg_sqlb2_code_fasttext_pos_leetcode_neg_sql_3k_eval_636d
mlfoundations-dev/b2_code_fasttext_pos_leetcode_neg_sql_3k_eval_636d
Precomputed model outputs for evaluation.
Evaluation Results
Summary
Metric
AIME24
AMC23
MATH500
MMLUPro
JEEBench
GPQADiamond
LiveCodeBench
CodeElo
CodeForces
Accuracy
19.7
60.0
73.8
30.2
44.6
43.3
33.1
10.6
14.4
AIME24
Average Accuracy: 19.67% ± 2.42%
Number of Runs: 10
Run
Accuracy
Questions Solved
Total Questions
1
10.00%
3
30
2
33.33%
10
30
3… See the full description on the dataset page: https://huggingface.co/datasets/mlfoundations-dev/b2_code_fasttext_pos_leetcode_neg_sql_3k_eval_636d.b2_code_fasttext_pos_leetcode_neg_sql_10kvgrout-leetcode-teacher-demos
vGROUT LeetCode teacher demonstrations
Cached teacher demonstrations used to warm up the
vGROUT gradient-routing experiments on the
ariahw/rl-rewardhacking LeetCode
environment. Each row is a full problem-specific completion. The kind column gives the
two demonstration types:
hack (215 rows): verified exploits of the run_tests loophole (hacked=True,
gt_pass=False).
solve (126 rows): correct solutions verified against the ground-truth tests
(gt_pass=True).
Why fewer… See the full description on the dataset page: https://huggingface.co/datasets/wassname/vgrout-leetcode-teacher-demos.b2_code_fasttext_pos_leetcode_neg_sql_0.3k_eval_636d
mlfoundations-dev/b2_code_fasttext_pos_leetcode_neg_sql_0.3k_eval_636d
Precomputed model outputs for evaluation.
Evaluation Results
Summary
Metric
AIME24
AMC23
MATH500
MMLUPro
JEEBench
GPQADiamond
LiveCodeBench
CodeElo
CodeForces
Accuracy
17.7
56.0
73.6
29.0
43.1
37.7
29.0
8.3
9.9
AIME24
Average Accuracy: 17.67% ± 1.77%
Number of Runs: 10
Run
Accuracy
Questions Solved
Total Questions
1
10.00%
3
30
2
16.67%
5
30
3… See the full description on the dataset page: https://huggingface.co/datasets/mlfoundations-dev/b2_code_fasttext_pos_leetcode_neg_sql_0.3k_eval_636d.LeetCode_YT_CC_CoT_SummaryLeetCode Information & YouTube Captions with CoT Summaries
Original data -> LimYeri/leetcode_with_youtube_captions
The original ['cc_content'] column had tokens that were too long and contained a lot of repetition, which necessitated summarization.
Consequently, our team (Project Team: CodeMind) summarized the ['cc_content'] column data using the Chain of Thought (CoT) technique with the gpt-3.5-turbo-0125 & gpt-4-turbo-2024-04-09 model. -> new column ['Summary']
b2_code_fasttext_pos_leetcode_neg_sql_10k_eval_636d
mlfoundations-dev/b2_code_fasttext_pos_leetcode_neg_sql_10k_eval_636d
Precomputed model outputs for evaluation.
Evaluation Results
Summary
Metric
AIME24
AMC23
MATH500
MMLUPro
JEEBench
GPQADiamond
LiveCodeBench
CodeElo
CodeForces
Accuracy
18.7
59.5
77.8
28.6
42.9
39.9
38.5
11.7
16.2
AIME24
Average Accuracy: 18.67% ± 0.97%
Number of Runs: 10
Run
Accuracy
Questions Solved
Total Questions
1
20.00%
6
30
2
20.00%
6
30
3… See the full description on the dataset page: https://huggingface.co/datasets/mlfoundations-dev/b2_code_fasttext_pos_leetcode_neg_sql_10k_eval_636d.b2_code_fasttext_pos_leetcode_neg_sql_1k_eval_636d
mlfoundations-dev/b2_code_fasttext_pos_leetcode_neg_sql_1k_eval_636d
Precomputed model outputs for evaluation.
Evaluation Results
Summary
Metric
AIME24
AMC23
MATH500
MMLUPro
JEEBench
GPQADiamond
LiveCodeBench
CodeElo
CodeForces
Accuracy
17.7
51.8
71.4
27.0
39.8
42.8
28.8
6.9
10.4
AIME24
Average Accuracy: 17.67% ± 1.16%
Number of Runs: 10
Run
Accuracy
Questions Solved
Total Questions
1
16.67%
5
30
2
23.33%
7
30
3… See the full description on the dataset page: https://huggingface.co/datasets/mlfoundations-dev/b2_code_fasttext_pos_leetcode_neg_sql_1k_eval_636d.b2_code_fasttext_pos_leetcode_neg_sql_eval_636d
mlfoundations-dev/b2_code_fasttext_pos_leetcode_neg_sql_eval_636d
Precomputed model outputs for evaluation.
Evaluation Results
Summary
Metric
AIME24
AMC23
MATH500
MMLUPro
JEEBench
GPQADiamond
LiveCodeBench
CodeElo
CodeForces
Accuracy
19.3
62.7
76.2
26.6
41.5
41.9
43.7
14.6
17.2
AIME24
Average Accuracy: 19.33% ± 1.14%
Number of Runs: 10
Run
Accuracy
Questions Solved
Total Questions
1
16.67%
5
30
2
23.33%
7
30
3
20.00%… See the full description on the dataset page: https://huggingface.co/datasets/mlfoundations-dev/b2_code_fasttext_pos_leetcode_neg_sql_eval_636d.