New DeltaPrompts method boosts VLM reasoning by targeting capability gaps

Researchers have introduced DeltaPrompts, a novel method to improve the distillation process for vision-language models (VLMs). The study reveals that many existing prompts provide minimal learning signals because the teacher and student models already produce similar outputs. DeltaPrompts addresses this by focusing on prompts that expose capability gaps, quantified by answer divergence. This approach involves a staged synthesis pipeline that generates high-divergence reasoning problems, leading to substantial performance gains, including up to a 15% relative improvement on models like Qwen3-VL-8B-Thinking across various reasoning benchmarks. AI

IMPACT Enhances VLM reasoning capabilities by improving distillation efficiency, potentially leading to more capable and compact models.

RANK_REASON The cluster contains a research paper detailing a new method for improving multimodal distillation. [lever_c_demoted from research: ic=1 ai=1.0]

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• alphaXiv
• arXiv
• CatalyzeX
• DagsHub
• DeltaPrompts
• Gotit.pub
• Hugging Face
• Jaehun Jung
• Qwen3-VL-8B-Thinking
• vision-language model

• paper
• model release

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