datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
codeforces-problems-7khardtests_problems
Dataset Card for HARDTESTS Problems
HARDTESTS is a competitive programming dataset containing 47,136 problems collected from 13 different Online Judges (OJs). Each problem includes a problem statement, numerous oracle code solutions, and a set of relatively reliable test cases. Note: Due to their large size, the test cases are stored in a separate dataset. This dataset is presented in the paper HardTests: Synthesizing High-Quality Test Cases for LLM Coding.
Project Page… See the full description on the dataset page: https://huggingface.co/datasets/sigcp/hardtests_problems.olympiad_style_integer_math_problems
Olympiad Math Corpus
Version: v2.1.1
Release date: 2026-05-03
59,486 synthetically generated olympiad-style math problems with verified integer answers and formal computation graphs.
Loading
from datasets import load_dataset
ds = load_dataset("mihailgribov/olympiad_style_integer_math_problems", split="train")
lemma_applicability is stored as list[{lemma, status}] rather than a sparse dict (required for Arrow-based consumers). To convert to a dict for local use:… See the full description on the dataset page: https://huggingface.co/datasets/mihailgribov/olympiad_style_integer_math_problems.ParallelKernelBench_Problems
ParallelKernelBench (benchmark)
Reference problems for ParallelKernelBench: a benchmark for LLM-generated multi-GPU CUDA kernels.
This dataset contains 87 reference implementations in reference/ and the input tensor specification in utils/input_output_tensors.py. Inputs are deterministic — reproduce them with create_input_tensor(rank, world_size, problem_id, base_shape, dtype, trial) from that file; you do not need stored .pt files.
Files
Path
Description… See the full description on the dataset page: https://huggingface.co/datasets/togethercomputer/ParallelKernelBench_Problems.ParallelKernelBench_Problems
ParallelKernelBench (benchmark)
Reference problems for ParallelKernelBench: a benchmark for LLM-generated multi-GPU CUDA kernels.
This dataset contains 87 reference implementations in reference/ and the input tensor specification in utils/input_output_tensors.py.
Files
Path
Description
data/problems.parquet
One row per problem (tabular access)
reference/*.py
Reference solution() implementations
utils/input_output_tensors.py
Input/output tensor… See the full description on the dataset page: https://huggingface.co/datasets/willychan21/ParallelKernelBench_Problems.math-story-problems
Math Story Problems Dataset
Dataset Description
This dataset contains mathematical word problems presented in multiple formats, from direct equations to complex story-based scenarios. It is designed for training and evaluating language models on mathematical reasoning tasks.
Dataset Structure
The dataset is split into three parts:
Train: 131,072 samples
Validation: 1,024 samples
Test: 3,072 samples
Features
{
"eq_qs": "string", # Equation… See the full description on the dataset page: https://huggingface.co/datasets/azminetoushikwasi/math-story-problems.logic-problems-reasoning-dataset
Dataset Card for my-distiset-a26cd729
This dataset has been created with distilabel.
Dataset Summary
This dataset contains a pipeline.yaml which can be used to reproduce the pipeline that generated it in distilabel using the distilabel CLI:
distilabel pipeline run --config "https://huggingface.co/datasets/sdiazlor/my-distiset-a26cd729/raw/main/pipeline.yaml"
or explore the configuration:
distilabel pipeline info --config… See the full description on the dataset page: https://huggingface.co/datasets/sdiazlor/logic-problems-reasoning-dataset.finecf-problems
Dataset Card for FineCF Problems
Dataset description
FineCF Problems is a dataset of 9,768 Codeforces problems, each paired with a cleaned, per-problem editorial explaining the solution approach. Problems span the full difficulty range (800 to 3500) and cover a wide variety of algorithmic topics including dp, graphs, math, greedy, data structures, and more.
You can load the dataset as follows:
from datasets import load_dataset
ds =… See the full description on the dataset page: https://huggingface.co/datasets/touristgpt/finecf-problems.Problem-Solving-Insights-Based-on-Kazakh-Traditions
🇰🇿 Problem-Solving Insights Based on Kazakh Traditions
📖 Overview
Problem-Solving Insights Based on Kazakh Traditions is a instruction-tuning dataset designed to bridge the gap between ancient Kazakh wisdom and modern societal challenges.
📊 Dataset Statistics
General Metrics
Metric
Count
Total Samples
8,005
Total Words (approx.)
4,030,925
Avg. Words per Sample
503
Word Count Distribution (Per… See the full description on the dataset page: https://huggingface.co/datasets/farabi-lab/Problem-Solving-Insights-Based-on-Kazakh-Traditions.math-problems-greedy-vs-best-of-n
Problem Solving Math Dataset - Greedy vs Best-of-N
This dataset contains mathematical problems and their solutions generated using two decoding strategies:
Greedy Decoding: Generates a single deterministic solution.
Best-of-N Decoding: Generates N solutions and selects the best one based on a scoring metric.
Dataset Structure
This dataset is created with a filtered subset of 20 level 1-3 problems from the MATH-500 dataset.
To have a balance across the levels, the… See the full description on the dataset page: https://huggingface.co/datasets/Tandogan/math-problems-greedy-vs-best-of-n.deepscale_r_math_problemsMix of agentica-org/DeepScaleR-Preview-Dataset and inclusionAI/Ring-lite-rl-data only MATH data, deduplicated
Results in total of 80k samples