Regularized Latent Dynamics Prediction is a Strong Baseline For Behavioral Foundation Models
arXiv:2603.15857v1 Announce Type: new 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 ortho
arXiv:2603.15857v1 Announce Type: new 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.
Executive Summary
The article introduces Regularized Latent Dynamics Prediction (RLDP) as a novel and effective baseline for Behavioral Foundation Models (BFMs) in zero-shot reinforcement learning. BFMs traditionally rely on complex representation learning objectives to generate state features that enable adaptability across unknown reward functions. However, the authors identify a critical issue: these objectives inadvertently increase state-feature similarity, diminishing the expressivity of the feature span. RLDP addresses this by introducing a simple orthogonality regularization, preserving feature diversity and enabling competitive or superior performance relative to existing complex methods. Empirical results demonstrate RLDP’s resilience in low-coverage scenarios, where prior approaches underperform. This work challenges the necessity of sophisticated representation learning for zero-shot RL, offering a more robust, simplified alternative.
Key Points
- ▸ RLDP introduces orthogonality regularization to counteract feature similarity issues
- ▸ Traditional BFMs depend on complex objectives that limit feature span expressivity
- ▸ Empirical validation shows RLDP outperforms or matches state-of-the-art in low-coverage contexts
Merits
Simplicity and Effectiveness
RLDP offers a straightforward solution to a persistent problem in BFM design without requiring additional computational or data overhead.
Demerits
Limited Scope
The study focuses primarily on zero-shot RL and may not generalize equally well to episodic or finite-horizon environments without further adaptation.
Expert Commentary
This paper makes a significant contribution by reframing a fundamental assumption in Behavioral Foundation Models: that complex representation learning is essential for zero-shot adaptability. The authors’ empirical demonstration that orthogonality regularization alone preserves feature diversity—and thus, span—challenges prevailing orthodoxy. The elegance of RLDP lies in its minimalism: it does not introduce new architectures, data requirements, or training paradigms, yet yields comparable or superior results. Moreover, the empirical evidence that prior methods degrade in low-coverage settings underscores a critical vulnerability in current BFM architectures that has been overlooked. This work is not merely incremental; it represents a paradigm shift in the design philosophy of BFMs, aligning practical performance with theoretical robustness. The implications extend beyond RL: any system reliant on latent feature generation for generalization may benefit from similar regularization strategies.
Recommendations
- ✓ Integrate RLDP as a baseline in comparative studies of BFMs across zero-shot RL benchmarks.
- ✓ Reproduce RLDP results in diverse domains (e.g., robotics, autonomous systems) to validate generalizability and identify domain-specific adaptations.
