orbit-ai/orbit-seeds

[!NOTE] For more information on the ORBIT dataset, go check out the preprint available at arxiv.org/abs/2604.01195.

<img src="https://huggingface.co/orbit-ai/orbit-4b-v0.1/resolve/main/orbit-with-name-logo.png" alt="Figure 1" width="500"/>

ORBIT: A Synthetic Training Dataset for Search Agents

    

ORBIT is a reasoning-intensive synthetic dataset with complex queries used for training search agents, generated without relying on any paid API services or manual annotation.

Orbit Seeds

Seed entities collected from English Wikipedia, organised by domain. Each record is a Wikipedia page that was used as a seed for reasoning-intensive question generation in the Orbit project.

Schema

Domains

Usage

from datasets import load_dataset

# Load a specific domain
ds = load_dataset("orbit-ai/orbit-seeds", "mathematics", split="train")
print(ds[0])

# Load all domains
domains = [
    "art", "code", "finance", "geography", "history", "law",
    "mathematics", "medicine", "music", "politics", "puzzles",
    "science_and_technology", "sports", "tv_shows_and_movies", "video_games",
]
for domain in domains:
    ds = load_dataset("orbit-ai/orbit-seeds", domain, split="train")
    print(f"{domain}: {len(ds)} seeds")

Citation

If you use ORBIT in your work, please cite our preprint on arXiv:

@misc{thakur2026orbit,
      title={ORBIT: Scalable and Verifiable Data Generation for Search Agents on a Tight Budget}, 
      author={Nandan Thakur and Zijian Chen and Xueguang Ma and Jimmy Lin},
      year={2026},
      eprint={2604.01195},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2604.01195}, 
}

Links