Theoretical Grounding of Out-Of-Distribution Detection With Reinforcement Learning Optimizer
On the map: Deep reinforcement learning methods → Deep RL algorithms and representations
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Deep RL algorithms and representations, one of 15 directions in Deep reinforcement learning methods. Builds on 18 of its 21 references that are in the map. First posted on arXiv 16 Jun 2026.
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18 of 21 references.
References · 18
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6 more
- 2020CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instancescited by 441
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- 2024Rethinking Out-of-Distribution Detection for Reinforcement Learning: Advancing Methods for Evaluation and Detection
- 2026OOD-RL-Bench: A Benchmark Framework for Out-of-Distribution Detection in Reinforcement Learning
- 2025Guaranteeing Out-Of-Distribution Detection in Deep RL via Transition Estimation
- 2025DEEDEE: Fast and Scalable Out-of-Distribution Dynamics Detection
- 2026Beyond Penalization: Diffusion-based Out-of-Distribution Detection and Selective Regularization in Offline Reinforcement Learning
- 2024Offline Reinforcement Learning with OOD State Correction and OOD Action Suppression
- 2026Generative OOD-regularized Model-based Policy Optimization
- 2025Variational OOD State Correction for Offline Reinforcement Learning