Toward Privileged Foundation Models:LUPI for Accelerated and Improved Learning

2026-05-08

AI Summary

This work addresses the challenges of large computational costs, slow convergence, and low data efficiency in foundational model pretraining. It proposes PIQL, a novel framework that introduces the Learning Using Privileged Information (LUPI) paradigm to tabular foundation model pretraining for the first time. PIQL leverages dataset-level statistics and data-generating programs as privileged information during training to guide representation learning and incorporates a context reconstruction architecture to transfer this knowledge effectively at inference time. Theoretical analysis demonstrates that privileged information reduces approximation error and accelerates convergence under limited data regimes. Empirical results confirm that PIQL significantly improves convergence speed, lowers training loss, and enhances generalization, thereby substantially reducing the reliance on both data volume and computational resources.

Abstract

Training foundation models is computationally intensive and often slow to converge.We introduce PIQL,Privileged Information for Quick and Quality Learning, the first framework to systematically integrate privileged information (PI) to simultaneously accelerate learning and improve generalization in tabular foundation models (TFMs). We construct two complementary forms of PI: (i) aggregate dataset-level statistics that reduce the burden on in-context learning, and (ii) encodings of the underlying data-generating program, providing knowledge beyond observable data. We further design an architecture that effectively transfers the train-time-only PI by learning to reconstruct it from observed context at inference. We provide a theoretical analysis characterizing conditions under which PI reduces the population-level approximation gap and accelerates convergence in finite-data regimes. Empirical evidence shows that PIQL enables TFMs to achieve faster convergence, lower final loss, and better generalization, in effect, reducing data and compute requirements. Our work establishes PI-guided pretraining as a principled and practical paradigm for improving the efficiency and performance of foundation models.

Problem

• foundation models
• privileged information
• accelerated learning
• generalization
• tabular data

Innovation

• Privileged Information
• Foundation Models
• Tabular Data
• Accelerated Learning
• Generalization