orbit-ai/orbit-stage-2-27k

⚠️ [!Warning] This is an unverified ORBIT dataset, only the Stage-2, and may contain data inaccuracies. The verified ORBIT dataset to use is orbit-ai/orbit-20k.

<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.

Overview

Training data for deep search — tasks requiring multi-step retrieval and reasoning over the web — is scarce. Existing datasets either target simple single-hop questions (NQ, HotpotQA) or require costly prerequisites such as knowledge-graph construction or human annotators.

ORBIT addresses this with a fully automatic, four-stage pipeline that generates reasoning-intensive question–answer pairs with short, verifiable answers grounded in real web evidence. The entire dataset was constructed using a frugal setup (a single laptop + free chat interfaces) at near-zero cost.

Key properties:

• —20,000+ verified question–answer training pairs
• —15 diverse domains (TV & Movies, Science & Technology, Art, History, Sports, Music, Video Games, Geography, Politics, Medicine, Finance, Law, Mathematics, Puzzles, Code)
• —4–6 reasoning steps required per question on average — harder than InfoSeek (3–5) and HotpotQA (2)
• —Short, verifiable answers averaging 3.5 tokens
• —4.36 verification URLs per pair, spanning Wikipedia, NIH, ScienceDirect, and many more sources
• —No paid APIs, no knowledge graphs, no human annotators required

Dataset Statistics

Dataset Schema

Each row in `orbit-ai/orbit-20k` contains the following fields:

Construction Pipeline

ORBIT is built with a four-stage, fully automatic pipeline:

Stage 1: Seed Creation
  └── 15 domains → 100 Wikipedia categories each (via OpenAI Deep Research)
      └── MediaWiki API → unique Wikipedia page titles as seeds

Stage 2: Multi-Hop Q&A Generation
  └── DeepSeek-V3.1 (chat.deepseek.com, DeepThink + Search enabled)
      └── Generates inverted question + short answer from each seed
          → 44,100 raw pairs

Stage 3: Self-Verification
  └── DeepSeek-V3.1 re-verifies each pair with independent web search
      └── Qwen3-4B-Instruct filters pairs with insufficient evidence
          → 27,100 pairs (61.5% pass rate)

Stage 4: External Verification
  └── Round 1: Qwen3-4B-Instruct judges against scraped URL content
  └── Round 2: gpt-oss-120b re-judges all failures from Round 1
  └── Manual review of borderline cases
          → 20,147 verified pairs

Questions are designed to be inverted (easier to verify given the answer) following the BrowseComp methodology, and explicitly require cross-source, multi-hop evidence.

Ethical Considerations

ORBIT is built from publicly accessible sources (Wikipedia categories and publicly retrievable webpages). We do not intentionally collect private or personally identifying information. The dataset is intended for factual question answering research only.

Because the pipeline relies on live web content and LLM-based generation, the data may inherit biases or framing effects from source webpages and generation models. Some answers may also become temporally brittle as web content evolves. We recommend treating ORBIT as a versioned research snapshot rather than an authoritative source of truth.

Browser automation (Selenium) was used to interact with DeepSeek Chat under a single authenticated personal account for academic, non-commercial purposes, with sequential rate-limited requests equivalent to manual usage.

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}, 
}

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