Multimodal language models (MLLMs) require large parameter capacity to align high-dimensional visual features with linguistic representations, making them computationally heavy and difficult to deploy efficiently. We introduce a progressive reparameterization strategy that compresses these models by gradually replacing dense feed-forward network blocks with compact Parameterized Hypercomplex Multiplication (PHM) layers. A residual interpolation schedule, together with lightweight reconstruction and knowledge distillation losses, ensures that the PHM modules inherit the functional behavior of their dense counterparts during training. This transition yields substantial parameter and FLOP reductions while preserving strong multimodal alignment, enabling faster inference without degrading output quality. We evaluate the approach on multiple vision-language models (VLMs). Our method maintains performance comparable to the base models while delivering significant reductions in model size and inference latency. Progressive PHM substitution thus offers an architecture-compatible path toward more efficient multimodal reasoning and complements existing low-bit quantization techniques.

翻译：多模态语言模型（MLLMs）需要庞大的参数量来对齐高维视觉特征与语言表示，导致其计算负担沉重且难以高效部署。本文提出一种渐进式重参数化策略，通过逐步将密集的前馈网络块替换为紧凑的参数化超复数乘法（PHM）层来压缩这些模型。结合残差插值调度以及轻量级重构与知识蒸馏损失，确保PHM模块在训练过程中继承其密集对应层的功能行为。这一转换实现了显著的参数与浮点运算量削减，同时保持了强大的多模态对齐能力，从而在不降低输出质量的前提下实现更快的推理速度。我们在多个视觉-语言模型（VLMs）上评估了该方法。实验表明，我们的方法在保持与基线模型相当性能的同时，显著减少了模型规模与推理延迟。因此，渐进式PHM替换为更高效的多模态推理提供了一条架构兼容的路径，并可作为现有低位量化技术的有效补充。