DVTR-KD: Distilling Vision-Language Models via Intermediate Visual Token Reconstruction

Haolan Wei, Jiahui Wang, Junli Zha, Xinkai Lu, Yunxiao Zou +2

Multimodal large language models (MLLMs) have achieved strong performance on diverse vision-language tasks, but their computational demands limit deployment under constrained budgets, particularly on edge devices. Many works focus on Knowledge Distillation (KD) and try to transfer knowledge from powerful teacher models to students. In addition to reducing model size, visual token compression provides a complementary route to efficiency, but introduces mismatched visual token counts that complicate direct token-wise knowledge transfer. To mitigate this, we propose DVTR-KD, a knowledge distillation framework based on \textbf{D}ense \textbf{V}isual \textbf{T}oken \textbf{R}econstruction, which reconstructs the teacher's dense visual tokens from the student's compressed tokens. We reconstruct visual states after the final LLM layer and at the input to an intermediate layer identified as important for visual-information readout. At the intermediate layer, reconstruction is supervised through the teacher's response predictions rather than by direct token-wise KL or MSE matching. An auxiliary attention-readout loss complements this response-level supervision by aligning how the student…