This article details the enhancements in Differential Transformer V2 (DIFF V2) over its predecessor. It focuses on the architecture's efficiency gains during decoding and training stability, achieved by adjusting query heads and eliminating certain normalization layers. Experimental results show reduced loss and gradient spikes in large language model training.