Latent world models fail to accurately capture environment dynamics
Understanding the limits of latent world models
Machine LearningArtificial Intelligence
Summary
Predicting how things change over time is important for making smart decisions. The authors looked at models that try to understand and predict environments internally, called latent world models. They found that even the best current models don't truly learn the actual dynamics that govern how the environment changes. This means these models solve tasks without fully understanding the underlying rules of the world they operate in.
What this means in practice
- •For reinforcement learning engineers: Assess and improve the reliability of latent world models for decision making using latent rollout consistency as a benchmark.
- •For robotics developers: Evaluate robot control systems to ensure their internal models reflect true environment dynamics, avoiding failures from flawed predictions.
Authors
Rodrigo Gonzalez Laiz, Steffen Schneider
Abstract
World models are widely proposed as core components for enabling agents to reason over long temporal and spatial horizons. Yet current evaluations often emphasize task performance while providing limited insight into whether learned world models acquire predictive latent dynamics that reflect the underlying structure of the environment. Here we introduce the concept of faithful latent world models as an equivariance condition between the environment's transition map and the learned latent dynamics. We propose latent rollout consistency (LRC) as a metric to evaluate action-conditioned latent prediction. Strikingly, we find that across the studied state of the art latent world models, none solves the task by actually faithfully modeling the dynamics of the environment.