# MCTQ

When a multimodal large language model is quantized to low bit-widths, text and vision tokens must learn one shared channel-wise transform, and their gradients can point in opposite directions or differ wildly in magnitude. MCTQ splits the reconstruction objective by modality, then combines the two gradients into a single update direction through a minimum-norm convex combination, keeping one shared transform and adding no inference overhead. On Qwen2.5-VL-7B at W4A6 it reaches 66.26 average accuracy, 1.30 points above OmniQuant; Qwen2.5-VL-32B reaches 66.51 in the same setting.

When a multimodal large language model is quantized to low bit-widths, text and vision tokens must learn one shared channel-wise transform, and their gradients can point in opposite directions or differ wildly in magnitude. MCTQ splits the reconstruction objective by modality, then combines the two gradients into a single update direction through a minimum-norm convex combination, keeping one shared transform and adding no inference overhead. On Qwen2.5-VL-7B at W4A6 it reaches 66.26 average accuracy, 1.30 points above OmniQuant; Qwen2.5-VL-32B reaches 66.51 in the same setting.