Scalable Reinforcement Learning via Adaptive Batch Scaling 事件

Scalable Reinforcement Learning via Adaptive Batch Scaling arXiv:2605.21557v2 Announce Type: replace-cross Abstract: Conventional wisdom holds that large-batch training is fundamentally incompatible with Reinforcement Learning (RL) - beyond a modest threshold, increasing batch sizes typically yields diminishing returns or performance degradation due to the inherent non-stationarity of the data distribution. We challenge this view by observing that non-stationarity is not a fixed property of RL,