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A benchmark study finding that a calibrated rule-based autoscaler beats six mainstream deep RL algorithms on cost across all tested workloads, with RL only showing benefits on bursty patterns at higher cost. The paper introduces RLScale-Bench to improve evaluation protocol and reproducibility.
Discusses practical challenges in scaling infrastructure for AI agent pipelines on a budget, highlighting the inadequacy of CPU/memory-based autoscaling for GPU inference workloads.