Dive Into the Implicit Biases of Low-rank Vision-language Alignment

• URL: http://arxiv.org/abs/2607.08194v1
• Date: Thu, 09 Jul 2026 07:50:49 GMT
• Title: Dive Into the Implicit Biases of Low-rank Vision-language Alignment
• Abstract summary: We find that low-rank alignment not only reduces computational costs but also outperforms full- parameter alignment on most benchmarks.<n> Empirically, we find that low-rank alignment shifts model behavior from hallucinatory to conservative and preserves per-language linear separability of visual features.<n>We establish two theorems showing that low-rank alignment induces preferences for parameter subspaces with flat gradients and feature subspaces robust to perturbations.
• Score: 22.80937850155209
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Vision-language alignment, the stage that bridges pretrained vision encoders and large language models, is widely treated as a form of pretraining requiring full-parameter updates. We challenge this view and investigate what happens when low-rank adaptation is applied to the LLM during this stage instead. We find that low-rank alignment not only reduces computational costs but also outperforms full-parameter alignment on most benchmarks. To understand this phenomenon, we systematically characterize the implicit biases introduced by low-rank adaptation during alignment. Empirically, we find that low-rank alignment shifts model behavior from hallucinatory to conservative and preserves per-token linear separability of visual features that full-parameter alignment disrupts, a phenomenon we term LS-curse. Geometrically, low rank aligned models exhibit more homogeneous and structurally stable visual representations, maintaining modality-specific knowledge rather than prematurely fusing entity-level semantics. Theoretically, we establish two theorems showing that low-rank alignment induces preferences for parameter subspaces with flat gradients and feature subspaces robust to perturbations, providing a principled explanation for the observed structure-preserving behavior. Extensive experiments cover ablation over 100 alignment configurations, three families of low-rank operators, and various rank, encoder, and other settings.

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