Training learned image compression (LIC) models entails navigating a challenging optimization landscape defined by the fundamental trade-off between rate and distortion. Standard first-order optimizers, such as SGD and Adam, struggle with \emph{gradient conflicts} arising from competing objectives, leading to slow convergence and suboptimal rate-distortion performance. In this work, we demonstrate that a simple utilization of a second-order quasi-Newton optimizer, \textbf{SOAP}, dramatically improves both training efficiency and final performance across diverse LICs. Our theoretical and empirical analyses reveal that Newton preconditioning inherently resolves the intra-step and inter-step update conflicts intrinsic to the R-D objective, facilitating faster, more stable convergence. Beyond acceleration, we uncover a critical deployability benefit: second-order trained models exhibit significantly fewer activation and latent outliers. This substantially enhances robustness to post-training quantization. Together, these results establish second-order optimization, achievable as a seamless drop-in replacement of the imported optimizer, as a powerful, practical tool for advancing the efficiency and real-world readiness of LICs.

翻译：训练学习型图像压缩（LIC）模型需要在由码率与失真之间的基本权衡所定义的复杂优化空间中导航。标准的一阶优化器，如SGD和Adam，难以处理由竞争目标引起的\emph{梯度冲突}，导致收敛缓慢和次优的码率-失真性能。在本工作中，我们证明，简单地使用二阶拟牛顿优化器\textbf{SOAP}，就能显著提高多种LIC模型的训练效率和最终性能。我们的理论和实证分析表明，牛顿预条件处理本质上解决了R-D目标函数固有的步内和步间更新冲突，从而促进了更快、更稳定的收敛。除了加速效果，我们还发现了一个关键的部署优势：经过二阶训练的模型表现出显著更少的激活值和潜在变量异常值。这大大增强了对训练后量化的鲁棒性。综上所述，这些结果表明，二阶优化作为一种可无缝替换现有优化器的强大实用工具，能够有效提升LIC模型的效率和实际部署准备度。