Task Scarcity and Label Leakage in Relational Transfer Learning

• URL: http://arxiv.org/abs/2603.29914v1
• Date: Tue, 31 Mar 2026 15:55:33 GMT
• Title: Task Scarcity and Label Leakage in Relational Transfer Learning
• Abstract summary: Training relational foundation models requires learning representations that transfer across tasks, yet available supervision is typically limited to a small number of prediction targets per database.<n>This problem causes learned representations to encode task-specific shortcuts that degrade transfer even within the same schema.<n>We introduce a gradient projection method that removes label-predictive directions from representation updates.<n>On RelBench, this improves within-dataset transfer by +0.145 AUROC on average, often recovering near single-task performance.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Training relational foundation models requires learning representations that transfer across tasks, yet available supervision is typically limited to a small number of prediction targets per database. This task scarcity causes learned representations to encode task-specific shortcuts that degrade transfer even within the same schema, a problem we call label leakage. We study this using K-Space, a modular architecture combining frozen pretrained tabular encoders with a lightweight message-passing core. To suppress leakage, we introduce a gradient projection method that removes label-predictive directions from representation updates. On RelBench, this improves within-dataset transfer by +0.145 AUROC on average, often recovering near single-task performance. Our results suggest that limited task diversity, not just limited data, constrains relational foundation models.

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