datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
NuminaMath-1.5-RL-Verifiable
Dataset Card for NuminaMath-1.5-RL-Verifiable
Dataset Summary
NuminaMath-1.5-RL-Verifiable is a curated subset of the NuminaMath-1.5 dataset, specifically filtered to support reinforcement learning applications requiring verifiable outcomes. This collection consists of 131,063 math word problems from the original dataset that meet strict filtering criteria: all problems have definitive numerical answers, validated problem statements and solutions, and come from… See the full description on the dataset page: https://huggingface.co/datasets/nlile/NuminaMath-1.5-RL-Verifiable.SWE-Bench-Verified-O1-reasoning-high-results
SWE-Bench Verified O1 Dataset
Executive Summary
This repository contains verified reasoning traces from the O1 model evaluating software engineering tasks. Using OpenHands + CodeAct v2.2, we tested O1's bug-fixing capabilities on the SWE-Bench Verified dataset, achieving a 28.8% success rate across 500 test instances.
Overview
This dataset was generated using the CodeAct framework, which aims to improve code generation through enhanced action-based reasoning.… See the full description on the dataset page: https://huggingface.co/datasets/AlexCuadron/SWE-Bench-Verified-O1-reasoning-high-results.Big-Math-RL-Verified
Big-Math: A Large-Scale, High-Quality Math Dataset for Reinforcement Learning in Language Models
Big-Math is the largest open-source dataset of high-quality mathematical problems, curated specifically for reinforcement learning (RL) training in language models. With over 250,000 rigorously filtered and verified problems, Big-Math bridges the gap between quality and quantity, establishing a robust foundation for advancing reasoning in LLMs.
Request Early Access to Private… See the full description on the dataset page: https://huggingface.co/datasets/SynthLabsAI/Big-Math-RL-Verified.simpleqa-verified
SimpleQA Verified
A 1,000-prompt factuality benchmark from Google DeepMind and Google Research, designed to reliably evaluate LLM parametric knowledge.
▶ SimpleQA Verified Leaderboard on Kaggle▶ Technical Report▶ Evaluation Starter Code
Benchmark
SimpleQA Verified is a 1,000-prompt benchmark for reliably evaluating Large Language Models (LLMs) on short-form factuality
and parametric knowledge. The authors from Google DeepMind and Google Research… See the full description on the dataset page: https://huggingface.co/datasets/google/simpleqa-verified.MedCalc-Bench-Verified
Updates
Updates to MedCalc-Bench Verified will be made on this page going forward.
Here is the github link for our repository: https://github.com/nikhilk7153/MedCalc-Bench-Verified
This is an updated version that is modified from MedCalc-Bench-v1.2.
While we have audited MedCalc-Bench Verified on mulitple occasions, should there by any corrections or enhancements, we will update with a new release and specify any changes.
The HuggingFace dataset and main branch will always… See the full description on the dataset page: https://huggingface.co/datasets/nsk7153/MedCalc-Bench-Verified.SWE-Bench-Verified-O1-native-tool-calling-reasoning-high-results
SWE-Bench Verified O1 Dataset
Executive Summary
This repository contains verified reasoning traces from the O1 model evaluating software engineering tasks. Using OpenHands + CodeAct v2.2, we tested O1's bug-fixing capabilities using their native tool calling capabilities on the SWE-Bench Verified dataset, achieving a 45.8% success rate across 500 test instances.
Overview
This dataset was generated using the CodeAct framework, which aims to improve code… See the full description on the dataset page: https://huggingface.co/datasets/AlexCuadron/SWE-Bench-Verified-O1-native-tool-calling-reasoning-high-results.TACO-verified
Introduction
This dataset contains verified solutions from the TACO dataset's training set. Solutions that fail to pass all the test cases are removed. Problems with no correct solution are also removed.
The solutions were executed on Intel E5-2620 v3 CPUs with the execution timeout set to 10 seconds.
Statistics in the training set
Dataset
# Problems
# Solutions
TACO
25443
1468722
TACO-verified
12898
1043251
Correct Ratio
50.69 %
71.03 %… See the full description on the dataset page: https://huggingface.co/datasets/likaixin/TACO-verified.WebInstruct-verified
General-Reasoner: Advancing LLM Reasoning Across All Domains
💻 Code |
📄 Paper |
📊 Dataset |
🤗 Model |
🌐 Project Page
Overview
Figure: Effectiveness of General-Reasoner trained with diverse verifiable reasoning questions using model-based verifier compared to baseline methods on various reasoning tasks.
General-Reasoner is a training paradigm for large language models (LLMs), designed to robustly enhance reasoning abilities across… See the full description on the dataset page: https://huggingface.co/datasets/TIGER-Lab/WebInstruct-verified.finqa-verified
FinQA: Financial Question Answering Dataset
Description
The FinQA dataset is designed to facilitate research and development in the area of question answering (QA) using financial texts.
It consists of a subset of QA pairs from a larger dataset, originally created through a collaboration between researchers from the University of Pennsylvania,
J.P. Morgan, and Amazon.The original dataset includes 8,281 QA pairs built against publicly available earnings reports of S&P… See the full description on the dataset page: https://huggingface.co/datasets/Aiera/finqa-verified.VisualWebInstruct-verified
🧠 VisualWebInstruct-Verified: High-Confidence Multimodal QA for Reinforcement Learning
VisualWebInstruct-Verified is a high-confidence subset of VisualWebInstruct, curated specifically for Reinforcement Learning (RL) and Reward Model training.
It contains verified multimodal question–answer pairs where correctness, reasoning quality, and image–text alignment have been explicitly validated.
This dataset is ideal for RLVR training pipelines.
📘 Dataset Overview… See the full description on the dataset page: https://huggingface.co/datasets/TIGER-Lab/VisualWebInstruct-verified.medical-o1-verifiable-problem
Introduction
This dataset features open-ended medical problems designed to improve LLMs' medical reasoning. Each entry includes a open-ended question and a ground-truth answer based on challenging medical exams. The verifiable answers enable checking LLM outputs, refining their reasoning processes.
For details, see our paper and GitHub repository.
Citation
If you find our data useful, please consider citing our work!
@misc{chen2024huatuogpto1medicalcomplexreasoning… See the full description on the dataset page: https://huggingface.co/datasets/FreedomIntelligence/medical-o1-verifiable-problem.trivia_qa_verified
TriviaQA Verified
A quality-verified subset of TriviaQA (Joshi et al., 2017) containing 4,170 question-answer pairs with confirmed correct answers, available in 5 languages.
Splits
Split
Language
Rows
english
English
4,170
mandarin
Mandarin Chinese
4,170
japanese
Japanese
4,170
arabic
Arabic
4,170
french
French
4,170
validation
English
3,381
The validation split contains a separate set of verified English questions (no overlap with other splits)… See the full description on the dataset page: https://huggingface.co/datasets/ai-safety-institute/trivia_qa_verified.SimpleQA-VerifiedSimpleQA Verified is a 1,000-prompt benchmark for reliably evaluating Large Language Models (LLMs) on short-form factuality and parametric knowledge. The authors from Google DeepMind and Google Research address various limitations of SimpleQA, originally designed by Wei et al. (2024) at OpenAI, including noisy and incorrect labels, topical biases, and question redundancy.
SimpleQA Verified was created to provide the research community with a more precise instrument to track genuine progress in… See the full description on the dataset page: https://huggingface.co/datasets/stalkermustang/SimpleQA-Verified.BixBench-Verified-50
BixBench-Verified-50
A curated subset of BixBench with 50 verified questions across 33 unique data capsules, designed for reliable evaluation of AI agents on computational biology tasks.
Overview
BixBench-Verified-50 was created to isolate real agent performance from benchmark issues. Starting from the full BixBench benchmark, we sampled questions and identified problematic ones. Some were removed entirely. For others, we revised the question text for clarity or corrected… See the full description on the dataset page: https://huggingface.co/datasets/phylobio/BixBench-Verified-50.simpleqa-verified-multilingual
SimpleQA Verified Multilingual
Multilingual translations of SimpleQA Verified, a 1,000-prompt factuality benchmark from Google DeepMind that evaluates short-form parametric knowledge (facts stored in model weights).
Source: google/simpleqa-verified (eval split, 1,000 examples)
Languages
Config
Language
Examples
ces
Czech
100
dan
Danish
100
deu
German
1,000
fra
French
100
ita
Italian
100
nld
Dutch
100
pol
Polish
100
spa
Spanish
100
More to… See the full description on the dataset page: https://huggingface.co/datasets/ellamind/simpleqa-verified-multilingual.k-verify-benchmark-v1
Part of the SZL Holdings governed estate — claims are designed to carry checkable receipts. Verification proves integrity & origin, never accuracy or performance.
K-Verify Benchmark v1
Author: Yachay / SZL Holdings · Version: 1.0.0 · Items: 100
K-Verify measures whether an AI's claimed factual answer is verifiable via a
receipt chain — not just whether it is correct. It is the first benchmark we
know of that scores provenance and honest refusal as… See the full description on the dataset page: https://huggingface.co/datasets/SZLHOLDINGS/k-verify-benchmark-v1.verified-sql-rewards
Verified SQL Rewards
A text-to-SQL corpus where every reward carries a machine-checkable proof
that it is correct.
Questions, all independently verified
109,306
Databases
1,400 across 7 schema families
Tables / data rows
4,400 / ~19.6 million
Unique (question, answer) pairs
102,764
Candidates refused and published
12,150
Verification pass rate
90.00%
Trivial baseline (always answer 0)
1.83%
Each item is a natural-language question, a gold SQL query… See the full description on the dataset page: https://huggingface.co/datasets/rasinmuhammed/verified-sql-rewards.scientific-verification
Scientific Verification Benchmark: NMC Cathodes
Dataset summary
The benchmark contains 50 scientific claims about NMC (lithium nickel manganese cobalt oxide) battery cathodes. Each claim is answered by Claude Opus 5, GPT 5.6 Luna and Gemini 3.1 Pro using a set of 20 open-access papers, producing 150 scored answers. The accompanying reference set contains 1,991 experiment-grounded measurements curated from 227 open-access papers, with experimental conditions and… See the full description on the dataset page: https://huggingface.co/datasets/GenData-Research/scientific-verification.MAPS_Verified
Dataset Card for Multilingual Benchmark for Global Agent Performance and Security
This is the first Multilingual Agentic AI Benchmark for evaluating agentic AI systems across different languages and diverse tasks. Benchmark enables systematic analysis of how agents perform under multilingual conditions. This dataset contains 550 instances for GAIA, 660 instances for ASB, 737 instances for Maths, and 1100 instances for SWE. Each task was translated into 10 target languages resulting… See the full description on the dataset page: https://huggingface.co/datasets/Fujitsu-FRE/MAPS_Verified.verified_wiki_historian_the_beatles_anthology_dataset_active
Verified-Wiki-Historian: The Beatles Anthology
Verified-Wiki-Historian (The Beatles Anthology) is a refined, citation-grounded instruction dataset for Beatles-specific historical question answering, summarization, and supervised fine-tuning.
This dataset is a cleaned and rebuilt refinement of:
Mungus451/verified_wiki_historian_the_beatles_anthology_dataset_active
The current release contains 4,000 instruction records focused on Beatles history, recording sessions, release… See the full description on the dataset page: https://huggingface.co/datasets/Mungus451/verified_wiki_historian_the_beatles_anthology_dataset_active.VeriTime
VeriTime: Time Series Reasoning via Process-Verifiable Thinking Data Synthesis and Scheduling for Tailored LLM Reasoning
This is the dataset associated with our paper:
Time Series Reasoning via Process-Verifiable Thinking Data Synthesis and Scheduling for Tailored LLM Reasoning
Jiahui Zhou, Dan Li, Boxin Li, Xiao Zhang, Erli Meng, Lin Li, Zhuomin Chen, Jian Lou, See-Kiong Ng
ICML 2026 | Paper
Dataset Construction Pipeline: TSRgen
TSRgen is an… See the full description on the dataset page: https://huggingface.co/datasets/HayleyZhou1113/VeriTime.verifiable-corpus
verifiable-corpus
This is the corpus from "Learning on the Job: Test-Time Curricula for Targeted Reinforcement Learning".
Code: https://github.com/jonhue/ttc
Introduction
We study how large language models (LLMs) can continually improve at reasoning on their target tasks at test-time. We propose an agent that assembles a task-specific curriculum, called test-time curriculum (TTC-RL), and applies reinforcement learning to continue training the model for its target task.… See the full description on the dataset page: https://huggingface.co/datasets/lasgroup/verifiable-corpus.verified-math-code-17k
Verified Math & Code, 17,000 rows
A math and code instruction dataset where every single row was mechanically checked before it was
allowed in. Not filtered by a heuristic, not scored by a model. Checked.
Two layers of verification, one per domain:
Every math answer was compared against an independent gold answer by exact, numeric and
symbolic (SymPy) comparison. If the worked solution did not arrive at the gold answer, the row
was dropped.… See the full description on the dataset page: https://huggingface.co/datasets/manifesta/verified-math-code-17k.VerifiableQFT
Verifiable Synethetic QFT Problems
This dataset provides the synthetic QFT problems and rejection sampled CoT samples used in Fine-Tuning Small Reasoning Models for Quantum Field Theory by N. Woodward et al..
The dataset consists of 2,588 synthetic Quantum Field Theory problems with auto-verifiable code solutions and 24,918 rejection-sampled chain-of-thought (CoT) solutions for supervised fine-tuning.
Dataset Summary
This dataset provides two complementary… See the full description on the dataset page: https://huggingface.co/datasets/nswoodward/VerifiableQFT.veritasQA
Dataset Card for VeritasQA
VeritasQA is a context- and time-independent QA benchmark for the evaluation of truthfulness in Language Models.
Dataset Summary
VeritasQA is a context- and time-independent truthfulness benchmark built with multilingual transferability in mind. It is intended to be used to evaluate Large Language Models on truthfulness in a zero-shot setting. VeritasQA comprises 353 question-answer pairs inspired by common misconceptions and falsehoods, not… See the full description on the dataset page: https://huggingface.co/datasets/projecte-aina/veritasQA.sft-tool-calling-structured-output-v1
vericava/sft-tool-calling-structured-output-v1
Dataset to train (SFT) 3-20B LLMs for tool calling and structured outputs/classifications.
Includes contents in English as well as some Japanese.
verified-research-reasoning-trajectories
Verified Research Reasoning Trajectories for RLVR
This repository is the public sample and schema repository for Ulam's research-level mathematical reasoning trajectories for reinforcement learning with verifiable rewards (RLVR), process supervision, judge training, proof criticism, and private evaluations.
Ulam Verified Research Reasoning Trajectories are proof-process data for RLVR. Each record contains a normalized research problem, a golden or partial-golden proof graph… See the full description on the dataset page: https://huggingface.co/datasets/ulamai/verified-research-reasoning-trajectories.SAFE-Verified-MultiHopQA
SAFE-Verified-MultiHopQA
Paper | Project Page
Overview
SAFE-Verified-MultiHopQA is released with the EMNLP 2026 paper SAFE: An LLM-as-Verifier Framework for Evidence-Grounded Multi-Hop Reasoning. It provides Knowledge Graph (KG) based, cleaned versions of three multi-hop question-answering benchmarks:
2WikiMultiHopQA (2wiki)
HotpotQA (hotpotqa)
MuSiQue (musique)
SAFE uses KG-grounded verification to identify questions with invalid or ungrounded reasoning. This… See the full description on the dataset page: https://huggingface.co/datasets/Daeyongkwon98/SAFE-Verified-MultiHopQA.chart-reasoning-verified
chart-reasoning-verified
Chart reasoning examples generated from an explicit latent representation.
The data, the question and the answer are computed before the chart is
drawn, so the image is a rendering of known ground truth rather than the
source of it. No model was asked to label anything.
Each row carries both a rendered chart and a text serialisation of the same
chart, so the set is usable for vision-language training and for text-only
language model training without… See the full description on the dataset page: https://huggingface.co/datasets/vinod-anbalagan/chart-reasoning-verified.verified-tool-use-dataset
Verified tool-use trajectories for LLM agents
This was a time-boxed experiment by an autonomous agent (Protogonos), now concluded. Nothing here is offered for sale or for hire, and no payment is accepted.
Multi-turn function-calling conversations for training and evaluating
tool-using agents — 48 trajectories across 16 domains, with every tool call
checked against its tool's JSON-Schema. The free sample in this repo is a real
slice of the full set: the viewer above renders it… See the full description on the dataset page: https://huggingface.co/datasets/protogonos/verified-tool-use-dataset.