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This paper presents an offline reinforcement learning framework for optimizing SLAM throughput control in warehouse fulfillment environments, balancing throughput maximization with downstream stability. The approach is algorithm-agnostic and demonstrates that the CQL policy improves system health by 22.97% and reduces throttling duration by 3.18%.