ZOMP: Zeroth-Order Multi-Modal Prompt Tuning for Vision-Language Models

• URL: http://arxiv.org/abs/2608.08060v1
• Date: Sat, 08 Aug 2026 10:55:58 GMT
• Title: ZOMP: Zeroth-Order Multi-Modal Prompt Tuning for Vision-Language Models
• Abstract summary: ZOMP is a query-efficient, fully forward-only prompt-tuning method.<n>It consistently outperforms prior BP-free prompt-tuning methods in both few-shot accuracy and query efficiency.<n>Our results show that jointly exploiting multimodality and low-rank structure is an effective route to practical, practical BP-free prompt tuning.
• Score: 14.64726819144383
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Fine-tuning vision-language models such as CLIP typically requires backpropagation (BP) through the full model, which is infeasible when only forward-pass access is available, as is common for memory-constrained edge devices and proprietary model deployments. Prior BP-free, zeroth-order prompt-tuning methods avoid this requirement but often tune prompts in a single modality or optimize over a search space large enough that convergence requires thousands of forward passes, which is impractical under realistic query budgets. We propose ZOMP (Zeroth-Order Multimodal Prompt tuning), a query-efficient, fully forward-only method that tunes deep prompts in both the vision and text branches of a frozen CLIP model using simultaneous perturbation stochastic approximation. ZOMP combines three ingredients: a cross-modal low-rank reparameterization that ties the two branches through a shared factor and keeps the effective search dimensionality small, a gradient-correction momentum term that stabilizes the noisy zeroth-order estimate, and a budget-indexed rank schedule that unlocks capacity as the query budget is spent. Across 13 vision-language benchmarks under a matched 5,000-query budget, ZOMP consistently outperforms prior BP-free prompt-tuning methods in both few-shot accuracy and query efficiency, and it generalizes better across base-to-new, cross-dataset transfer, and out-of-distribution settings. Our results show that jointly exploiting multimodality and low-rank structure is an effective route to practical, query-efficient BP-free prompt tuning.

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