datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
TravelPlanner
TravelPlanner Dataset
TravelPlanner is a benchmark crafted for evaluating language agents in tool-use and complex planning within multiple constraints. (See our paper for more details.)
Introduction
In TravelPlanner, for a given query, language agents are expected to formulate a comprehensive plan that includes transportation, daily meals, attractions, and accommodation for each day.
TravelPlanner comprises 1,225 queries in total. The number of days and hard constraints… See the full description on the dataset page: https://huggingface.co/datasets/osunlp/TravelPlanner.QUEST-RL-Data
QUEST RL Data
Project Page | Paper | GitHub
Training split for the QUEST / DeepResearch RL recipe. Each row includes prompt, reward_model, extra_info, and related fields.
Dataset columns
Column
Description
data_source
Source tag (e.g. deepresearch_tasks)
prompt
list[{"role", "content"}] chat-style input
reward_model
Reward configuration (Python literal string; some rows embed numpy-like array(...) and need custom parsing)
extra_info
Extra metadata… See the full description on the dataset page: https://huggingface.co/datasets/osunlp/QUEST-RL-Data.QUEST-SFT-Data-Objective-Script
QUEST SFT Data Objective Script
Project Page | Paper | GitHub
Supervised fine-tuning split for QUEST / DeepResearch objective tasks. Each row includes the user prompt, a rule-style reward_model, extra_info, and the objective task category. The corresponding objective evaluation scripts are provided separately under eval_scripts/.
This dataset follows the same broad schema style as osunlp/QUEST-RL-Data: each row includes prompt, reward_model, extra_info, and rl_task_category. The… See the full description on the dataset page: https://huggingface.co/datasets/osunlp/QUEST-SFT-Data-Objective-Script.QUEST-SFT-Data-Objective
QUEST SFT Data (Objective)
Project Page | Paper | GitHub
Objective-style supervised fine-tuning trajectories for QUEST (tool-using assistant format). This dataset is part of the QUEST family, designed to train deep research agents with fully synthetic tasks.
Split: train
Columns: messages (list[{role, content}])
Load
from datasets import load_dataset
ds = load_dataset("osunlp/QUEST-SFT-Data-Objective", split="train", streaming=True)
row = next(iter(ds))… See the full description on the dataset page: https://huggingface.co/datasets/osunlp/QUEST-SFT-Data-Objective.QUEST-SFT-Data-Open-ended
QUEST SFT Data (Open-ended)
Project Page | Paper | GitHub
Open-ended supervised fine-tuning trajectories for QUEST (tool-using assistant format). Split: train. Columns: messages (list[{role, content}]).
Load
from datasets import load_dataset
ds = load_dataset("osunlp/QUEST-SFT-Data-Open-ended", split="train", streaming=True)
row = next(iter(ds))
print(row.keys())
QUEST Family
Type
Resources
35B checkpoints
RL, MT+SFT, MT, SFT
30B checkpoints… See the full description on the dataset page: https://huggingface.co/datasets/osunlp/QUEST-SFT-Data-Open-ended.AutoSDT-5K
AutoSDT: Scaling Data-Driven Discovery Tasks Toward Open Co-Scientists
AutoSDT-5K is an automatically constructed dataset of 5,404 coding tasks for data-driven discovery that covers four scientific disciplines and 756 unique Python packages. Expert feedback on a subset of 256 tasks shows the quality of AutoSDT-5K: 93% of the collected tasks are ecologically valid, and 92.2% of the synthesized programs are functionally correct. To the best of our knowledge, AutoSDT-5K is the only… See the full description on the dataset page: https://huggingface.co/datasets/osunlp/AutoSDT-5K.ACuRL
ACuRL Curriculum Tasks
Paper | GitHub | Models
This dataset contains curriculum tasks generated by ACuRL, an Autonomous Curriculum Reinforcement Learning framework for continually adapting computer-use agents to target environments with zero human data.
The dataset includes two splits, qwen3vl and uitars, corresponding to two base agents: Qwen3-VL-8B-Instruct and UI-TARS-1.5-7B. In both splits, curriculum tasks are generated with GPT-5.
Each row is a natural-language task… See the full description on the dataset page: https://huggingface.co/datasets/osunlp/ACuRL.Chinese_Multi-Emotion_Dialogue_Dataset
Chinese_Multi-Emotion_Dialogue_Dataset
📄 Description
This dataset contains 4159 Chinese dialogues annotated with 8 distinct emotion categories. The data is suitable for emotion recognition, sentiment analysis, and other NLP tasks involving Chinese text.
Data Sources:
Daily Conversations: Captured from natural, informal human conversations.
Movie Dialogues: Extracted from diverse Chinese-language movies.
AI-Generated Dialogues: Synthesized using advanced… See the full description on the dataset page: https://huggingface.co/datasets/osuih/Chinese_Multi-Emotion_Dialogue_Dataset.