datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
RL-seed-Decensor-Difficultyprompt-difficulty
Prompt Difficulty Assessment
Prompt difficulty plays a critical role in the performance of large language models (LLMs).
Assessing this difficulty is essential for selecting training examples, evaluating model capabilities, and optimizing routing and reasoning strategies.
Yet, no standardized framework exists for comparing prompt difficulty across domains.
This report proposes a method to quantify prompt difficulty using multiple LLMs and introduces a composite difficulty score for… See the full description on the dataset page: https://huggingface.co/datasets/agentlans/prompt-difficulty.dapo-math-17k-difficulty-qwen3-1.7b-base-k16
DAPO-Math-17k difficulty under Qwen3-1.7B-Base (K=16)
For each of the 17,398 problems in the DAPO-Math-17k train set, how many of
K=16 samples from the untrained base model are correct.
The headline: 57.27% of problems are solved 0 out of 16 times, and not one
problem is solved 16 out of 16. Difficulty here is entirely one-sided.
Why count per problem instead of reporting mean accuracy
In group-relative RL (GRPO and its relatives), a prompt group whose K responses… See the full description on the dataset page: https://huggingface.co/datasets/RyanYr/dapo-math-17k-difficulty-qwen3-1.7b-base-k16.labeled_numina_difficultyWe also include data of labeling difficulty from NUMINA, in the following files: labeled_amc_aime_0_-1.json, labeled_math_0_-1.json, labeled_olympiads_0_-1.json.
OpenMath-Difficulty-Annotated
📐 OpenMath-Difficulty-Annotated
🚀 Overview
OpenMath-Difficulty-Annotated is a curated subset of OpenMathInstruct-2 containing 10,176 math problems, enhanced with precise difficulty metadata.
While the original solutions are preserved from NVIDIA's dataset, we employed a 120B Parameter Model (LLM-as-a-Judge) to analyze and grade every single problem on a scale of 1 to 5.
This allows developers of Small Language Models (1B-3B) to filter out "Olympiad-level" noise… See the full description on the dataset page: https://huggingface.co/datasets/HAD653/OpenMath-Difficulty-Annotated.lime-nlp-difficulty
lime-nlp Difficulty Estimation Math Datasets collection
Unofficial reformatted version of lime-nlp/difficulty-estimation-math-datasets,
which contains math problems and the Qwen 2.5 7B MATH model's success rates at solving those problems.
The combined dataset has been split into 80% training and 20% testing data.
Fields:
row_id: the row number of each dataset entry, starting at 0
input: the math question from the dataset
output: the correct answer (ground truth)… See the full description on the dataset page: https://huggingface.co/datasets/agentlans/lime-nlp-difficulty.prompt-difficulty-model-ratings
Prompt Difficulty Model Ratings
Dataset contains approximately 100 000 ChatGPT prompts from agentlans/chatgpt
The prompts were rated for difficulty using the large language models:
allenai/Olmo-3-7B-Instruct
google/gemma-3-12b-it
ibm-granite/granite-4.0-h-tiny
meta-llama/Llama-3.1-8B-Instruct
microsoft/phi-4
mistralai/Ministral-3-8B-Instruct-2512nvidia/NVIDIA-Nemotron-Nano-9B-v2
Qwen/Qwen3-8B
swiss-ai/Apertus-8B-Instruct-2509
tiiuae/Falcon-H1-7B-Instruct
Prompt template:… See the full description on the dataset page: https://huggingface.co/datasets/agentlans/prompt-difficulty-model-ratings.DeepSeek-R1-Distill-Qwen-1.5-difficultyprompt-difficulty-mixed
Prompt Difficulty Meta-Analysis
Introduction
The difficulty of large language model (LLM) prompts varies widely, from simple queries to complex multi-step reasoning tasks.
This study develops a consistent, data-driven difficulty score for English ChatGPT prompts, using classifiers trained on labelled difficulty datasets.
The goal is to improve automated prompt difficulty classification.
Methods
Detailed methods
Several methods were used to quantify the… See the full description on the dataset page: https://huggingface.co/datasets/agentlans/prompt-difficulty-mixed.ORZ-MATH-57k-Filter-difficultyBatsResearch-Cross-Difficulty
BatsResearch/Cross-Difficulty Prompt Dataset
Unofficial, reformatted version of BatsResearch/Cross-Difficulty.
The multiple choice questions are included in the prompt instead of separate columns.
The difficulty scores have been normalized per dataset (mean 0, standard deviation 1) and split into 80% training, 20% testing
[!WARNING]
This dataset contains data from well-known testing datasets, so models trained directly on this data will be contaminated.
Don't repost the dataset… See the full description on the dataset page: https://huggingface.co/datasets/agentlans/BatsResearch-Cross-Difficulty.prompt-difficulty-datasets
Prompt Difficulty Datasets
passage-difficulty-simplifier-dataset-chirhoclean_openthought312_difficulty_9_filterd元データ: https://huggingface.co/datasets/open-thoughts/OpenThoughts3-1.2M
diffculty 9でさらにフィルタリングしたもの
データ件数: 14,339
平均トークン数: 13370
最大トークン数: 16,808
合計トークン数: 191,708,678
ファイル形式: JSONL
ファイルサイズ: 723.9 MB
math_difficulty_level_5_problemsdifficulty_testdifficulty-5B
Difficulty Split (Zero Context Medium)
This dataset mirrors the local layout used in training:
train/*.jsonl
val_id/*.jsonl
val_ood/*.jsonl
Each JSONL row contains fields like problem, question, and solution (the latter includes an Answer: segment near the end).
Load with datasets (streaming)
from datasets import load_dataset
repo = "goodevening/difficulty-5B"
train = load_dataset(
"json",
data_files={"train": f"hf://datasets/{repo}/train/*.jsonl"}… See the full description on the dataset page: https://huggingface.co/datasets/goodevening/difficulty-5B.clean_openthought312_difficulty_9_qwentoken元データ: https://huggingface.co/datasets/LLMTeamAkiyama/clean_openthought312_difficulty_9_filterd
データ件数: 14,339
平均トークン数: 13,367
最大トークン数: 16,805
合計トークン数: 191,665,652
ファイル形式: JSONL
ファイル分割数: 3
合計ファイルサイズ: 724.7 MB
加工内容：
**tokenizeをQwen235B-A22Bで再度トークン化したものを出力
使用したコード
https://github.com/LLMTeamAkiyama/0-data_prepare/blob/master/src/openthoughts3/clean_openthoughts3_9_qwentoken.ipynb
ece6514-group3-acereason-15k-difficultychat-difficulty
Chat Difficulty
Collection of conversations evaluated using Qwen 3 series.
Prompt template:
You are an AI evaluator tasked with rating the overall difficulty of a complete human–AI conversation (all user messages and AI responses) on a 1–10 scale based on how challenging it would be for an AI to handle effectively.
<conversation>
[CONVERSATION]
</conversation>
Evaluate the conversation as a whole, considering:
* Clarity of user intent
* Required context and reliance on prior turns… See the full description on the dataset page: https://huggingface.co/datasets/agentlans/chat-difficulty.Qwen-2.5-3B_difficulty_based_data