Efficient Adaptive Data Acquisition via Pretrained Belief Representations

2026-06-23

AI Summary

Existing adaptive data acquisition methods often suffer from inefficient policy learning due to reliance on biased posterior approximations or inadequate exploitation of model representations. This work proposes POLAR, a novel framework that decouples representation learning from policy learning by leveraging pretrained predictive foundation models to encode belief states. POLAR unifies Bayesian experimental design, Bayesian optimization, and active learning within a single coherent framework. By integrating amortized policy learning with task-specific utility functions, the approach substantially reduces the required number of training samples and consistently outperforms state-of-the-art amortized methods across diverse tasks, significantly enhancing the scalability and efficiency of data acquisition.

Abstract

Learning effective policies for adaptive data acquisition remains challenging: posterior-based methods rely on surrogate models and posterior approximations that can be misspecified or biased, while direct policy-learning methods map from historical observations and fail to exploit available model representations, making learning harder. We introduce policy learning with belief representations (POLAR), based on the insight that optimal data acquisition depends on the observation history only through a sufficient belief state. Specifically, POLAR decouples representation learning from policy learning by leveraging pretrained predictive foundation models as belief-state encoders, training a policy head on top of their representations. This yields a simple, unified amortised policy learning framework for Bayesian experimental design, Bayesian optimisation, and active learning, differing only in the task-specific utility used to train the policy. Empirically, we find that POLAR outperforms state-of-the-art amortised methods across diverse tasks while requiring far fewer training samples, demonstrating a significant step in the scalability and efficiency of amortised data acquisition.

Problem

• adaptive data acquisition
• policy learning
• belief representations
• amortised inference
• Bayesian experimental design

Innovation

• belief representation
• amortised policy learning
• pretrained foundation models
• adaptive data acquisition
• Bayesian experimental design