DistilVDR: A Compact End-to-End Visual Document Retriever via Dual-Student Distillation
Visual document retrieval (VDR) is dominated by multi-billion-parameter models that are slow to index at full corpus scale and expensive to serve. Prior compression routes either train a smaller multi-vector encoder from scratch or distil only the query side; neither yields a compact single-vector retriever end-to-end. We present DistilVDR, a 524M end-to-end VDR system distilled bilaterally from a single 8B vision-language teacher under a pointwise cosine alignment loss. All supervision comes fr
Record details
Published: 10 August 2026
Source: HuggingFace Daily Papers
Category: Research
Topics: Safety & alignment · Children & education
Retrieved: 13 August 2026
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How to cite this record
ethics.ai (10 August 2026), “DistilVDR: A Compact End-to-End Visual Document Retriever via Dual-Student Distillation,” evidence record 18758, https://ethics.ai/record/18758 (originally published by HuggingFace Daily Papers).
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