A new adaptive tensor parallelism method, PAT, has been developed to enhance the efficiency of Reinforcement Learning from Human Feedback (RLHF) training by dynamically adjusting configurations during the generation stage. This innovation has shown to significantly reduce generation latency and overall training iteration times, indicating a potential boost in model training efficiency for AI applications.