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Reassessing Global Gradient-Norm Imbalance in BLIP Fine-Tuning Across Physical Domains

arXiv · AI, language, vision and robotics · article · Sep 20, 2026 · UTC

Imbalanced gradient magnitudes between the visual and language pathways of a vision-language model are often treated as a defect to be corrected. We test that premise for one family of correction, deliberately excluding adaptive, signal-driven schemes (e.g. BalGrad, OGM, PMR, CGGM), which are a mechanistically distinct class outside this study's scope. Measuring the language-to-visual gradient-norm ratio, reported in parameter-normalised form, across nine fine-tuning conditions, three seeds, and three captioning datasets spanning distinct physical domain shifts -- underwater, aerial, radiologi

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