Deployed RL agents receiving evaluative rewards face inherent non-stationarity and should engage in continual learning rather than following a train-then-fix approach.
In-Context Learning can Perform Continual Learning Like Humans.arXiv preprint 2509.22764,
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Position: Deployed Reinforcement Learning should be Continual
Deployed RL agents receiving evaluative rewards face inherent non-stationarity and should engage in continual learning rather than following a train-then-fix approach.