arXiv:2606.04029v1 Announce Type: new Abstract: Reinforcement Learning (RL) has received increasing attention and adoption in real-world use cases. Most of these systems follow a train-then-fix paradigm, where trained agents do not learn while interacting with the world until performance degrades and retraining becomes necessary. In this position paper, we argue that deploying an agent that is incapable of optimality, but receives an evaluative reward signal, is inherently a continual RL problem. We identify four sources of non-stationarity after deployment that necessitate never-ending learni
The increasing deployment of AI systems in real-world scenarios highlights the limitations of current static training paradigms, making perpetual learning a critical next step.
This shift from a 'train-then-fix' to a continual learning model will fundamentally alter how AI systems are designed, deployed, and maintained, impacting their reliability and adaptability.
AI agents will transition from static, pre-trained entities to dynamic systems capable of continuous adaptation post-deployment, driven by real-world interaction and non-stationarity.
- · AI developers
- · Robotics companies
- · SaaS providers leveraging AI
- · Industries deploying AI for critical operations
- · Companies reliant on static AI models
- · Legacy AI maintenance service providers
Deployed AI systems will become more robust and adaptable, reducing the need for costly and disruptive retraining cycles.
This will accelerate the adoption of autonomous AI agents in complex, rapidly changing environments, collapsing more workflows.
The continuous learning paradigm could lead to new ethical considerations and regulatory frameworks surrounding perpetually updating AI.
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