Research Paper#Vision-Language Models, Fine-tuning, Mask Fine-Tuning (MFT)🔬 ResearchAnalyzed: Jan 3, 2026 19:15
Rethinking Fine-Tuning for Vision-Language Models
Published:Dec 28, 2025 20:41
•1 min read
•ArXiv
Analysis
This paper introduces Mask Fine-Tuning (MFT) as a novel approach to fine-tuning Vision-Language Models (VLMs). Instead of updating weights, MFT reparameterizes the model by assigning learnable gating scores, allowing the model to reorganize its internal subnetworks. The key contribution is demonstrating that MFT can outperform traditional methods like LoRA and even full fine-tuning, achieving high performance without altering the frozen backbone. This suggests that effective adaptation can be achieved by re-establishing connections within the model's existing knowledge, offering a more efficient and potentially less destructive fine-tuning strategy.
Key Takeaways
- •Proposes Mask Fine-Tuning (MFT) for Vision-Language Models (VLMs).
- •MFT reparameterizes the model using learnable gating scores instead of weight updates.
- •Demonstrates superior performance compared to LoRA and full fine-tuning.
- •Highlights the importance of re-establishing connections within existing model knowledge for effective adaptation.
- •Offers a more efficient and potentially less destructive fine-tuning approach.
Reference
“MFT consistently surpasses LoRA variants and even full fine-tuning, achieving high performance without altering the frozen backbone.”