FAST: A Framework for Aligned Sampling and Training in Parallel Reinforcement Learning

FAST addresses sampling inefficiency in autonomous driving reinforcement learning by introducing Dynamic Parallel Sampling Alignment to decouple episode termination from sampling loops. It achieves up to 1.78 times wall-clock speedup over single-clip baselines while maintaining statistical unbiasedness through Scaled Mask-Padding Optimization.