arXiv:2605.23565v1 Announce Type: new Abstract: Reinforcement learning agents often exhibit unintended goal-directed behaviour outside their training distribution, but we currently lack a principled understanding of how such agents will generalise to novel environments based on their training history. We address this gap for agents trained sequentially on one or more tasks. We study over 100 sequential training pipelines, evaluating behaviour across over 250 out-of-distribution environments. We find that salient features drive generalisation, and that goals learnt early in training can persist
The proliferation of complex AI systems, particularly in reinforcement learning, necessitates a deeper understanding of their generalization capabilities to ensure robust and predictable behavior in diverse environments.
A strategic reader should care because understanding how AI agents generalize is crucial for deploying reliable and adaptable autonomous systems, impacting everything from robotics to complex decision-making processes.
This research provides a foundational step towards predicting and controlling how AI agents trained on sequential tasks will perform in novel, unseen scenarios, shifting development from trial-and-error to principled design.
- · AI researchers
- · Robotics developers
- · Autonomous system manufacturers
- · AI safety and ethics organizations
- · Developers of unreliable AI systems
- · Industries reliant on opaque AI black boxes
Improved understanding of AI agent generalization will accelerate the development of more robust and transferable AI models.
This enhanced predictability will enable broader deployment of AI in critical real-world applications where generalizability is paramount, such as autonomous vehicles or complex industrial automation.
The insights into 'salient features' and 'persistent goals' could lead to new architectural paradigms for AI that inherently encode better generalization capabilities, influencing future AI hardware and software design.
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Read at arXiv cs.LG