Deep Research Pretraining via Predictive Navigation
Deep research agents are often trained on expensive, environment-grounded tool-use trajectories that require repeated retrieval, document inspection, and report evaluation. We introduce Deep Research Pretraining (DRP), an offline framework that derives predictive navigation supervision from naturally occurring evidence structures. Given a citation-bearing or hyperlinked passage, DRP constructs a proxy research objective, recovers linked evidence and graph-related alternatives, and converts them into search-open-write trajectories. This teaches models what to search for, which documents to inspect, and how to synthesize evidence, without a live retrieval environment or executed policy roll...
| Authors | Jiang Zhou, Zhiyuan Fan, Xing Wu et al. |
|---|---|
| Primary category | cs.CL |
| Published | 1 August 2026 |
| Last updated at source | 1 August 2026 |
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