#adaptive-training News & Analysis

Scalable Reinforcement Learning via Adaptive Batch Scaling

Researchers propose Adaptive Batch Scaling (ABS), a technique that dynamically adjusts batch sizes during reinforcement learning training by measuring policy stability through a novel 'Behavioral Divergence' metric. The approach challenges the conventional belief that large batches are incompatible with RL, demonstrating that combining larger networks with larger batch sizes can achieve superior performance when batch size adapts to training phase stability.