datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
OpenMLE-Tasks
OpenMLE Tasks
📄 Paper
•
🌐 Project
•
💻 Code
•
🤗 Models
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📚 SFT Traces
OpenMLE Tasks provides machine-learning Task environments. The public SFT trajectories are released separately in OpenMLE-SFT-Traces. These resources accompany the paper Frontis-MA1: Training an AI4AI Model towards Recursive Self-Improvement in Machine Learning Engineering and the OpenRSI code release.
Release form
What is included… See the full description on the dataset page: https://huggingface.co/datasets/FrontisAI/OpenMLE-Tasks.proofwriter
Dataset Card for "proofwriter"
More Information needed
foliohttps://github.com/Yale-LILY/FOLIO
@article{han2022folio,
title={FOLIO: Natural Language Reasoning with First-Order Logic},
author = {Han, Simeng and Schoelkopf, Hailey and Zhao, Yilun and Qi, Zhenting and Riddell, Martin and Benson, Luke and Sun, Lucy and Zubova, Ekaterina and Qiao, Yujie and Burtell, Matthew and Peng, David and Fan, Jonathan and Liu, Yixin and Wong, Brian and Sailor, Malcolm and Ni, Ansong and Nan, Linyong and Kasai, Jungo and Yu, Tao and Zhang, Rui and Joty, Shafiq and… See the full description on the dataset page: https://huggingface.co/datasets/tasksource/folio.multi_task_multi_modal_knowledge_retrieval_benchmark_M2KR
PreFLMR M2KR Dataset Card
Dataset details
Dataset type:
M2KR is a benchmark dataset for multimodal knowledge retrieval. It contains a collection of tasks and datasets for training and evaluating multimodal knowledge retrieval models.
We pre-process the datasets into a uniform format and write several task-specific prompting instructions for each dataset. The details of the instruction can be found in the paper. The M2KR benchmark contains three types of tasks:… See the full description on the dataset page: https://huggingface.co/datasets/BByrneLab/multi_task_multi_modal_knowledge_retrieval_benchmark_M2KR.ropedia-xperience-10m-task-suite-artifacts
Ropedia Xperience-10M Task Suite Artifacts
This dataset repository stores small derived artifacts for the Ropedia
Xperience-10M task-suite project: metrics, predictions, manifests, reports,
figures, website JSON, public-safe Qwen3-Omni diagnostic outputs, and the
Cosmos3-Nano plus Cosmos3-Super diagnostic packages.
Project Identity
The Project identity mark is shared across the GitHub README, GitHub Pages
dashboard, Hugging Face Space, artifact dataset, model… See the full description on the dataset page: https://huggingface.co/datasets/cy0307/ropedia-xperience-10m-task-suite-artifacts.finance-tasks
Adapting LLMs to Domains via Continual Pre-Training (ICLR 2024)
This repo contains the evaluation datasets for our paper Adapting Large Language Models via Reading Comprehension.
We explore continued pre-training on domain-specific corpora for large language models. While this approach enriches LLMs with domain knowledge, it significantly hurts their prompting ability for question answering. Inspired by human learning via reading comprehension, we propose a simple method to… See the full description on the dataset page: https://huggingface.co/datasets/AdaptLLM/finance-tasks.osworld_v2_tasks
OSWorld V2 Task Classes
This gated dataset contains the official root-level task_*.py Python task classes for OSWorld V2.
The public GitHub repository keeps the task loader, helper utilities, and documentation. The task implementations are gated to reduce benchmark leakage and to help prevent evaluated agents from finding task answers, setup logic, or evaluator details online while executing a task.
Download from the public repository root with:
uvx --from huggingface_hub hf… See the full description on the dataset page: https://huggingface.co/datasets/xlangai/osworld_v2_tasks.task_data
QuantCodeEval
A benchmark for evaluating LLM coding agents on quantitative-strategy code
reproduction from finance research papers.
Status: Anonymous artifact for the 30-task benchmark.
Release mirrors
The release is mirrored at two anonymous locations:
Hugging Face Datasets — complete anonymous release:
https://huggingface.co/datasets/quantcodeeval/task_data
anonymous.4open.science — browseable mirror:
https://anonymous.4open.science/r/QuantCodeEval-Anonymous… See the full description on the dataset page: https://huggingface.co/datasets/quantcodeeval/task_data.esci
Dataset Card for "esci"
ESCI product search dataset
https://github.com/amazon-science/esci-data/
Preprocessings:
-joined the two relevant files
-product_text aggregate all product text
-mapped esci_label to full name
@article{reddy2022shopping,
title={Shopping Queries Dataset: A Large-Scale {ESCI} Benchmark for Improving Product Search},
author={Chandan K. Reddy and Lluís Màrquez and Fran Valero and Nikhil Rao and Hugo Zaragoza and Sambaran Bandyopadhyay and Arnab Biswas and Anlu… See the full description on the dataset page: https://huggingface.co/datasets/tasksource/esci.wmt20_mlqe_task1
Dataset Card for WMT20 - MultiLingual Quality Estimation (MLQE) Task1
Dataset Summary
From the homepage:
This shared task (part of WMT20) will build on its previous editions to further examine automatic methods for estimating the quality of neural machine translation output at run-time, without relying on reference translations. As in previous years, we cover estimation at various levels. Important elements introduced this year include: a new task where sentences are… See the full description on the dataset page: https://huggingface.co/datasets/wmt/wmt20_mlqe_task1.RoboTwin-LeRobot-unseen-tasks-cross-embRecursive-Task-Synthesis
Recursive Task Synthesis
This dataset contains 37,484 validated command-line task instances produced
through recursive task synthesis. Public identifiers are opaque and stable.
metadata/tasks.parquet: one searchable row per task instance.
metadata/shard_manifest.jsonl: TAR sizes and SHA256 checksums.
data/tasks-*.tar: sanitized runnable task packages.
The searchable task rows include:
instruction: contents of instruction.md.
task_toml: contents of task.toml.
solution:… See the full description on the dataset page: https://huggingface.co/datasets/Zhongzhi1228/Recursive-Task-Synthesis.task09_close_refrigeratorThis dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v3.0",
"robot_type": "mobileai_robot",
"total_episodes": 250,
"total_frames": 71211,
"total_tasks": 1,
"chunks_size": 1000,
"data_files_size_in_mb": 100,
"video_files_size_in_mb": 500,
"fps": 30,
"splits": {
"train": "0:250"
},
"data_path": "data/chunk-{chunk_index:03d}/file-{file_index:03d}.parquet",
"video_path":… See the full description on the dataset page: https://huggingface.co/datasets/kiroaiseoul/task09_close_refrigerator.Tmax-Tasks-Clean
Tmax-Tasks-Clean
New: longlongcheck (2026-09-14)
Use configuration longlongcheck, split longlongcheck, for the 431-task snapshot combining the selected Codex and Claude Code repairs with passing historical GPT-6 terminal solutions. The existing splits below retain their earlier data.
From the latest local 452-task repaired snapshot, this split holds out the requested 15 old-pass/current-fail tasks, four additional tasks without any passing current GPT-6 replay… See the full description on the dataset page: https://huggingface.co/datasets/Fzz1/Tmax-Tasks-Clean.Taskbench
TaskBench: Benchmarking Large Language Models for Task Automation
Introduction
TaskBench is a benchmark for evaluating large language models (LLMs) on task automation. Task automation can be formulated into three critical stages: task decomposition, tool invocation, and parameter prediction. This complexity makes data collection and evaluation more challenging compared to common NLP tasks. To address this challenge, we propose a comprehensive evaluation framework… See the full description on the dataset page: https://huggingface.co/datasets/microsoft/Taskbench.SWE-Rebench-Tasks-Clean
SWE-Rebench-Tasks-Clean
1,317 verified-solvable, contamination-controlled software-engineering tasks for terminal-agent RL training.
Adapted from nebius/SWE-rebench-V2 (real GitHub issue → PR tasks with executable test contracts) into the TerminalWorld task format. Companion dataset to Fzz1/SWE-Smith-Seeds-Clean, same layout.
Every task is a directory containing:
file
content
instruction.md
the issue text the agent sees (plus linked issue discussion where available)… See the full description on the dataset page: https://huggingface.co/datasets/Fzz1/SWE-Rebench-Tasks-Clean.Math-RL-Tasks
Ulam AI Math RL Tasks
Forty original, verifier-backed mathematical reasoning tasks packaged as ten
independent RL environments. The collection spans advanced graduate exercises,
research-style exact computation and structural generalization problems in
algebraic geometry, arithmetic geometry, combinatorics, topology, probability
and spectral analysis.
Each suite pairs a runnable rl_env/ with a preserved blind_run/ by
GPT-5.6 Sol Pro. The model name describes the evaluation actor… See the full description on the dataset page: https://huggingface.co/datasets/ulamai/Math-RL-Tasks.Recursive-Task-Synthesis-Trajectories
Recursive Task Synthesis Trajectories
This dataset contains 327,189 completed agent trajectories collected on
recursively synthesized command-line tasks. Public identifiers are opaque and
stable.
The trajectory JSON retains messages, actions, observations, and token counts.
Token-level log-probability arrays and duplicated debug/session captures are
excluded from the public packages.
metadata/trajectories.parquet: searchable trajectory metadata.
metadata/shard_manifest.jsonl:… See the full description on the dataset page: https://huggingface.co/datasets/Zhongzhi1228/Recursive-Task-Synthesis-Trajectories.Arena-G1-Loco-Manipulation-Task
Dataset Description:
The Arena-G1-Loco-Manipulation-Task dataset is multimodal collections of trajectories generated in Isaac Lab. It supports humanoid (G1) loco-manipulation task in IsaacLab-Arena environment. Each entry provides the full context (state, vision, language, action) needed to train and evaluate generalist robot policies for box pick and place task.
Dataset Name
# Trajectories
G1 Loco-Manipulation Task
50
This dataset is ideal for behavior cloning… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Arena-G1-Loco-Manipulation-Task.multi_task_multi_modal_knowledge_retrieval_benchmark_M2KR_CN
PreFLMR M2KR Dataset Card
Dataset details
Dataset type:
M2KR is a benchmark dataset for multimodal knowledge retrieval. It contains a collection of tasks and datasets for training and evaluating multimodal knowledge retrieval models.
We pre-process the datasets into a uniform format and write several task-specific prompting instructions for each dataset. The details of the instruction can be found in the paper. The M2KR benchmark contains three types of tasks:… See the full description on the dataset page: https://huggingface.co/datasets/BByrneLab/multi_task_multi_modal_knowledge_retrieval_benchmark_M2KR_CN.logical-entailmenthttps://github.com/google-deepmind/logical-entailment-dataset
@inproceedings{
evans2018can,
title={Can Neural Networks Understand Logical Entailment?},
author={Richard Evans and David Saxton and David Amos and Pushmeet Kohli and Edward Grefenstette},
booktitle={International Conference on Learning Representations},
year={2018},
url={https://openreview.net/forum?id=SkZxCk-0Z},
}
task07_open_refrigeratorThis dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v3.0",
"robot_type": "mobileai_robot",
"total_episodes": 279,
"total_frames": 106292,
"total_tasks": 2,
"chunks_size": 1000,
"data_files_size_in_mb": 100,
"video_files_size_in_mb": 200,
"fps": 30,
"splits": {
"train": "0:279"
},
"data_path": "data/chunk-{chunk_index:03d}/file-{file_index:03d}.parquet",
"video_path":… See the full description on the dataset page: https://huggingface.co/datasets/kiroaiseoul/task07_open_refrigerator.Audio-Video-Engineering-Agentic-Tasks-1M
Audio/Video Engineering Agentic Tasks (1M)
Abstract
A highly specialized dataset comprising 1,029,459 in-context troubleshooting prompts and execution commands built for the deepest levels of media production. Unlike standard datasets that simulate clean, theoretical instructions, this matrix captures the chaotic, highly-detailed, and conversational reality of professional audio engineers, composers, and video editors mid-session. It is engineered to train multimodal AI… See the full description on the dataset page: https://huggingface.co/datasets/yatin-superintelligence/Audio-Video-Engineering-Agentic-Tasks-1M.task06_pickup_beaker_and_move_to_refrigeratorThis dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v3.0",
"robot_type": "mobileai_robot",
"total_episodes": 200,
"total_frames": 131573,
"total_tasks": 2,
"chunks_size": 1000,
"data_files_size_in_mb": 100,
"video_files_size_in_mb": 500,
"fps": 30,
"splits": {
"train": "0:200"
},
"data_path": "data/chunk-{chunk_index:03d}/file-{file_index:03d}.parquet",
"video_path":… See the full description on the dataset page: https://huggingface.co/datasets/kiroaiseoul/task06_pickup_beaker_and_move_to_refrigerator.Creative-Professionals-Agentic-Tasks-1M
Creative Professionals Agentic Tasks (1M)
Abstract
A massive-scale, high-fidelity synthetic task dataset comprising 1,070,917 agentic command operations across 36 creative, technical, and engineering software environments. This dataset is engineered exclusively to stress-test, evaluate, and fine-tune multimodal AI agents designed for Agent Environment operation, complex software interaction, and multi-step reasoning within deep software infrastructures.… See the full description on the dataset page: https://huggingface.co/datasets/yatin-superintelligence/Creative-Professionals-Agentic-Tasks-1M.SLT-Task2-Post-ASR-Speaker-Tagging
Dataset Name: Dataset for ASR Speaker-Tagging Corrections (Speaker Diarization)
Description
This dataset is pairs of erroneous ASR output and speaker tagging, which are generated from a ASR system and speaker diarization system.
Each source erroneous transcription is paired with human-annotated transcription, which has correct transcription and speaker tagging.
SEGment-wise Long-form Speech Transcription annotation (SegLST), the file format used in the CHiME challenges… See the full description on the dataset page: https://huggingface.co/datasets/GenSEC-LLM/SLT-Task2-Post-ASR-Speaker-Tagging.task1_1This dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v3.0",
"fps": 30,
"features": {
"observation.state": {
"dtype": "float32",
"shape": [
14
],
"names": [
"arm_left_joint_0",
"arm_left_joint_1",
"arm_left_joint_2",
"arm_left_joint_3",
"arm_left_joint_4"… See the full description on the dataset page: https://huggingface.co/datasets/takeru01/task1_1.agent-task-litecoder-terminal-rl-preview
Apptainer pool for hamishivi/agent-task-litecoder-terminal-rl-preview
This repository hosts tmax-compatible SIF images and a unified download manifest. Training data and task archives are in hamishivi/agent-task-litecoder-terminal-rl-preview. The manifest includes earlier images hosted under hamishivi and new images hosted under TMaxxx; the downloader selects the correct repository and immutable commit for each image.
Apptainer images
The pool currently contains… See the full description on the dataset page: https://huggingface.co/datasets/TMaxxx/agent-task-litecoder-terminal-rl-preview.QuALITY
Dataset Card for "QuALITY"
@article{bowman2022quality,
title={QuALITY: Question Answering with Long Input Texts, Yes!},
author={Bowman, Samuel R and Chen, Angelica and He, He and Joshi, Nitish and Ma, Johnny and Nangia, Nikita and Padmakumar, Vishakh and Pang, Richard Yuanzhe and Parrish, Alicia and Phang, Jason and others},
journal={NAACL 2022},
year={2022}
}
task08_takeout_and_put_beakerThis dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v3.0",
"robot_type": "mobileai_robot",
"total_episodes": 251,
"total_frames": 96871,
"total_tasks": 1,
"chunks_size": 1000,
"data_files_size_in_mb": 100,
"video_files_size_in_mb": 500,
"fps": 30,
"splits": {
"train": "0:251"
},
"data_path": "data/chunk-{chunk_index:03d}/file-{file_index:03d}.parquet",
"video_path":… See the full description on the dataset page: https://huggingface.co/datasets/kiroaiseoul/task08_takeout_and_put_beaker.