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This position paper argues that the standard "train-then-fix" paradigm in Reinforcement Learning is insufficient for real-world applications due to inherent non-stationarity after deployment. The authors contend that deployed agents receiving evaluative reward signals must engage in continual learning to maintain optimality and adapt to changing environments. Moving away from static models toward never-ending adaptation is presented as a necessary evolution for robust, long-term RL systems.
Position: Deployed Reinforcement Learning should be Continual
arXiv:2606.04029v2 Announce Type: replace 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 learning, and highlight why the best deployed agents never stop adapting. We analyze successful examples of continual RL in the real world, and present the community with the advantages and measures to move away from the current train-then-fix paradigm.
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 learning, and highlight why the best deployed agents never stop adapting. We analyze successful examples of continual RL in the real world, and present the community with the advantages and measures to move away from the current train-then-fix paradigm.
Accepted to the ICML 2026 Position Paper Track. See this https URL
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
arXiv:2606.04029 [cs.LG]
(or arXiv:2606.04029v2 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2606.04029 Focus to learn more arXiv-issued DOI via DataCite
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