Regularized Latent Dynamics Prediction is a Strong Baseline For Behavioral Foundation Models

• URL: http://arxiv.org/abs/2603.15857v1
• Date: Mon, 16 Mar 2026 19:39:27 GMT
• Title: Regularized Latent Dynamics Prediction is a Strong Baseline For Behavioral Foundation Models
• Abstract summary: Behavioral Foundation Models (BFMs) produce agents with the capability to adapt to any unknown reward or task.<n>These methods are only able to produce near-optimal policies for the reward functions that are in the span of some pre-existing state features.<n>We propose an approach, Regularized Latent Dynamics Prediction (RLDP), that can match or surpass state-of-the-art complex representation learning methods for zero-shot RL.
• Score: 35.088440282359024
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Behavioral Foundation Models (BFMs) produce agents with the capability to adapt to any unknown reward or task. These methods, however, are only able to produce near-optimal policies for the reward functions that are in the span of some pre-existing state features, making the choice of state features crucial to the expressivity of the BFM. As a result, BFMs are trained using a variety of complex objectives and require sufficient dataset coverage, to train task-useful spanning features. In this work, we examine the question: are these complex representation learning objectives necessary for zero-shot RL? Specifically, we revisit the objective of self-supervised next-state prediction in latent space for state feature learning, but observe that such an objective alone is prone to increasing state-feature similarity, and subsequently reducing span. We propose an approach, Regularized Latent Dynamics Prediction (RLDP), that adds a simple orthogonality regularization to maintain feature diversity and can match or surpass state-of-the-art complex representation learning methods for zero-shot RL. Furthermore, we empirically show that prior approaches perform poorly in low-coverage scenarios where RLDP still succeeds.

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