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A reinforcement learning (RL) recommender VERA is presented that formulates vertical memory scaling as a Markov Decision Process and trains an agent on 3353 real Prometheus traces and demonstrates that an observation-driven RL recommender could outperform retrospective heuristics for dynamic memory scaling.
