Investigating Cross-Modal Skill Injection: Scenarios, Methods, and Hyperparameters
Vision-Language Models (VLMs) have demonstrated remarkable proficiency in general multi-modal understanding; yet they struggle to efficiently acquire continually evolving domain-specific skills. Conventional approaches to enhancing VLM capabilities, such as Supervised Fine-Tuning (SFT), require extensive dataset curation and substantial computational resources. Model merging has emerged as an efficient alternative that enables the transfer of domain-specific expertise from Large Language Models (LLMs) to VLMs without incurring additional training data requirements or significant computational overhead. Unlike conventional merging of homogeneous LLMs, which mainly aggregates existing capab...
| Authors | Zhiyu Xu, Lean Wang, Yuanxin Liu et al. |
|---|---|
| Primary category | cs.CL |
| Published | 19 May 2026 |
| Last updated at source | 19 May 2026 |
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