Theoretical Grounding of Out-Of-Distribution Detection With Reinforcement Learning Optimizer

2026-06-15

AI Summary

This work addresses the challenge of continual adaptation in dynamic open-world settings, where models must generalize under covariate shift while reliably rejecting semantically out-of-distribution (OOD) inputs. Existing approaches lack explicit modeling of future environmental shifts. To bridge this gap, the paper establishes the first theoretical framework for dynamic OOD detection and introduces a reinforcement learning (RL)-guided optimizer that augments standard gradient descent with an RL-based correction term, explicitly minimizing long-term semantic OOD false positive rates. By decomposing generalization error into components attributable to model evolution and environmental dynamics over time, the method directly optimizes future generalization capability. Empirical results demonstrate that the proposed optimizer consistently outperforms conventional counterparts in both future domain generalization and semantic OOD rejection, thereby validating the efficacy of the theoretical framework.

Abstract

Out-of-distribution (OOD) detection in dynamic open-world environments requires a model to continually adapt to evolving data distributions while generalizing to covariate-shifted inputs and rejecting semantic-shifted OOD examples. Most existing OOD detection methods optimize only the current-step objective and do not explicitly account for how post-deployment environment changes affect future OOD behavior. In this paper, we establish a theoretical grounding for dynamic OOD detection using a reinforcement learning (RL)-guided optimizer that explicitly favors updates that reduce the semantic OOD false positive rate over time. We develop a novel augmented optimizer that uses an RL-guided correction term on top of standard gradient descent (GD) and show its improvement over both future-domain generalization and semantic-OOD rejection. We analyze temporal error decomposition in terms of model-change and environment-change generalization errors and develop a new theoretical framework for comparing the generalization errors under both GD and RL-guided optimizers.

Problem

• out-of-distribution detection
• dynamic open-world environments
• semantic shift
• covariate shift
• future-domain generalization

Innovation

• out-of-distribution detection
• reinforcement learning optimizer
• dynamic open-world
• semantic shift
• generalization error