ORBIT: Scalable and Verifiable Data Generation for Search Agents on a Tight Budget
Search agents, which integrate language models (LMs) with web search, are becoming crucial for answering complex user queries. Constructing training datasets for deep research tasks, involving multi-step retrieval and reasoning, remains challenging due to expensive human annotation, or cumbersome prerequisites. In this work, we introduce ORBIT, a training dataset with 20K reasoning-intensive queries with short verifiable answers, generated using a frugal framework without relying on paid API services. The modular framework relies on four stages: seed creation, question-answer pair generation, and two stages of verification: self and external. ORBIT spans 15 domains and each training pair ...
| Authors | Nandan Thakur, Zijian Chen, Xueguang Ma et al. |
|---|---|
| Primary category | cs.CL |
| Published | 1 April 2026 |
| Last updated at source | 2 April 2026 |
Models and datasets referencing this paper
- orbit-ai/orbit-stage-3-27kDataset
- orbit-ai/orbit-20kDataset
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