The exponential growth of deep learning models imposes severe memory constraints on GPUs, significantly increasing training costs. The prevailing solution for addressing memory constraints is tensor offloading, exemplified by ZeRO Infinity, which leverages GPUs, CPUs, and NVMe SSDs to enable large-scale model training. However, ZeRO-Infinity faces significant performance bottlenecks due to memory access imbalance, coarse-grained tensor transfers, NVMe bandwidth and latency limits, and software complexity. Compute Express Link (CXL) emerges as a promising technology for building disaggregated memory pools, yet its integration into large-scale training remains underexplored.This paper introduces an efficient CXL memory pool into the system for tensor offloading and leveraging CXL protocol features for hardware acceleration. The proposed design incorporates NUMA-aware memory allocation and a Dynamic Adaptive Pipelining (DAP) strategy to enhance communication–computation overlap, along with a hybrid object-based memory management scheme to mitigate fragmentation and improve allocation efficiency. Experimental evaluation on an 8- GPU system with CXL Type-3 expansion cards demonstrates up to 72.7% throughput improvement, 62.9% latency reduction, and support for training 1.14× larger models compared with SSD based ZeRO-Infinity. This is the first work to integrate CXL with ZeRO-Infinity for large-scale training, offering practical insights for future CXL-based heterogeneous systems.