Pretraining in Relational Databases Across Tasks and Entity Types

Relational databases support many predictive tasks over shared histories, yet relational deep learning models are typically trained separately for each target and entity type. We study whether a conventional relational encoder can instead be pretrained once on many query-defined forecasting tasks and reused. Query-Conditioned Relational Pretraining (QCRP) generates future-prediction objectives from structured query specifications and trains them jointly through a shared query-conditioned predictor. Evaluated on RelBench, frozen QCRP encoders improve over existing relational pretraining objectives on standard tasks. Because standard targets largely overlap with pretraining objectives, we introduce 24 strictly held-out tasks, where QCRP also demonstrates strong transfer. A single encoder pretrained across entity types remains competitive with type-specific specialists. We further find that query-defined tasks share low-dimensional structure, which the query encoder captures to support zero-shot prediction for unseen queries, although its advantage over nearest-query substitution is limited. These results suggest that task-diverse pretraining is a practical route to reusable database-level encoders, and that task specification is a promising interface for future work.