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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.
Efficiency in GUI agents is as crucial as task success, with recent advancements converging on innovative strategies like selective reading and hybrid execution models.
Static GPU partitioning alone can't solve underutilization, but fine-grained CPU offloading over Nvlink-C2C can bridge the gap.
