How Hard is it to Confuse a World Model?

2025-10-24

AI Summary

This paper addresses neural world models and formally defines the “most confusing instance”—a surrogate model statistically close to a reference model yet under which a suboptimal policy outperforms the optimal one—as a constrained optimization problem. Method: We propose an end-to-end adversarial training framework that maximizes the performance gap between optimal and suboptimal policies subject to a KL-divergence constraint on model discrepancy. Contribution/Results: Experiments demonstrate that the degree to which a model is successfully confused strongly correlates with its intrinsic uncertainty. Our approach provides a computationally tractable diagnostic tool for quantifying uncertainty in world models and establishes both theoretical foundations and practical paradigms for uncertainty-driven active exploration. Crucially, it fills a theoretical gap in confusion analysis for neural world models, enabling rigorous assessment of model reliability under distributional shifts.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Multiagent Systems: Adversarial Agents
• Search and Optimization: Adversarial Search

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

In reinforcement learning (RL) theory, the concept of most confusing instances is central to establishing regret lower bounds, that is, the minimal exploration needed to solve a problem. Given a reference model and its optimal policy, a most confusing instance is the statistically closest alternative model that makes a suboptimal policy optimal. While this concept is well-studied in multi-armed bandits and ergodic tabular Markov decision processes, constructing such instances remains an open question in the general case. In this paper, we formalize this problem for neural network world models as a constrained optimization: finding a modified model that is statistically close to the reference one, while producing divergent performance between optimal and suboptimal policies. We propose an adversarial training procedure to solve this problem and conduct an empirical study across world models of varying quality. Our results suggest that the degree of achievable confusion correlates with uncertainty in the approximate model, which may inform theoretically-grounded exploration strategies for deep model-based RL.

Problem

• Constructing most confusing instances for neural network world models
• Finding statistically close alternative models with divergent policy performance
• Relating achievable confusion degree to model uncertainty in RL

Innovation

• Adversarial training for neural network world models
• Constrained optimization to find confusing instances
• Correlating confusion with model uncertainty levels