Cross-layer key-value (KV) compression has been found to be effective in efficient inference of large language models (LLMs). Although they reduce the memory consumption of the KV cache, such methods usually introduce non-negligible performance degradation. In this work, we aim to enhance the performance of YOCO, a cross-layer KV compression method that shares the KVs of the middle layer with the top-half layers. We propose YOCO++, an enhanced YOCO that incorporates a weighted residual connection between the KVs of each bottom-half layer and the bottom layer. Compared to YOCO, YOCO++ increases model capacity while maintaining the same training and inference efficiency. Our experiments show that YOCO++ achieves state-of-the-art performance among the cross-layer KV compression methods at a 50% KV cache compression rate, outperforming the standard Transformer.
@article{arxiv.2604.13556,
title = {YOCO++: Enhancing YOCO with KV Residual Connections for Efficient LLM Inference},
author = {You Wu and Ziheng Chen and Yizhen Zhang and Haoyi Wu and Chengting Yu and Yuchi Xu and Wenbo Su and Bo Zheng and Kewei Tu},
journal= {arXiv preprint arXiv:2604.13556},
year = {2026}
}