OpenSearcher: Democratizing Deep Search through a Fully Offline Pipeline with Programmatic Verification

Deep search capabilities have become an indispensable competency for frontier large language model (LLM) agents, yet training high-performance deep search agents remains constrained by three key challenges: as synthesized reasoning graphs grow in complexity, verifying answer correctness and uniqueness becomes increasingly difficult to scale; heavy dependence on online search APIs makes data synthesis costly and hard to scale; and the lack of fully open synthesis pipelines severely hinders reproducibility.To bridge this gap, we introduce OpenSearcher, the first deep search agent training pipeline with zero online API dependency throughout the entire data synthesis and model training process, built upon two core technical innovations: (1) \textit{Distinguishing Set-grounded QA synthesis with programmatic verification}: we compute minimal Distinguishing Sets on Wikidata to uniquely identify target entities, recursively expand them into reasoning DAGs, and transform the structured graphs into natural language questions via progressive generation and fuzzification. Answer uniqueness is deterministically verified through SPARQL conjunctive queries, complemented by lightweight LLM filtering for linguistic quality. (2) \textit{Fully offline training pipeline}, which builds an offline corpus of tens of millions of documents from publicly downloadable Wikidata, Wikipedia, and Web Crawl Dumps, and completes trajectory synthesis, SFT cold start, and reinforcement learning entirely offline. Experimental results demonstrate that OpenSearcher achieves strong performance across deep search benchmarks, establishing a new state of the art among fully offline methods, for example, achieving 32.3\% on BrowseComp and 72.8\% on GAIA. We fully open-source the complete data synthesis pipeline to democratize offline deep search agent research and foster a more transparent, collaborative ecosystem.