When Better Teachers Don't Make Better Students: Revisiting Knowledge Distillation for CLIP Models in VQA
Researchers have investigated the effectiveness of knowledge distillation for CLIP-style models in visual question answering tasks. They found that stronger teacher models do not always produce better student models, and existing distillation frameworks can actually degrade performance in downstream tasks. This challenges prevailing assumptions about knowledge distillation and suggests new directions for designing efficient multimodal models.
Researchers have investigated the effectiveness of knowledge distillation for CLIP-style models in visual question answering tasks. They found that stronger teacher models do not always produce better student models, and existing distillation frameworks can actually degrade performance in downstream tasks. This challenges prevailing assumptions about knowledge distillation and suggests new directions for designing efficient multimodal models.
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Why it matters: This matters to AI researchers because it highlights the limitations of current knowledge distillation methods and points to potential areas for improvement in building more efficient multimodal models.
Source: https://arxiv.org/abs/2511.17886
This article was originally published at: https://arxiv.org/abs/2511.17886