arXiv Machine Learning By Pranaya Jajoo, Harshit Sikchi, Siddhant Agarwal, Amy Zhang, Scott Niekum, Martha White

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

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The paper introduces Regularized Latent Dynamics Prediction (RLDP), a method that adds orthogonality regularization to self‑supervised next‑state prediction in latent space. RLDP maintains feature diversity, matching or surpassing complex representation learning approaches for zero‑shot reinforcement learning. It also performs robustly in low‑coverage data settings where prior methods fail.

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arXiv AI
Aug 19

Towards Zero-Shot Task Transfer with Neurosymbolic World Models

The paper introduces a neurosymbolic world model that separates observation reconstruction from reward prediction, enabling the model to adapt zero‑shot to new reward functions defined over a shared symbolic state space. This approach addresses the task‑dependency of traditional neural world models, which learn latent representations tied to specific training tasks. Experiments show that the neurosymbolic formulation generalises more strongly than purely neural methods.

By Isidoro Tamassia, Lennert De Smet, Giuseppe Marra