ScAle: Attention Head Scaling as a Minimal Adapter for Spatial Reasoning in Vision Language Models
arXiv:2606.29579v1 Announce Type: cross Abstract: Spatial reasoning remains a persistent challenge for many vision language models (VLMs), and improving it typically requires fine-tuning with substantial additional parameters. Our preliminary analysis reveals that rescaling activations in selected transformer layers-without modifying pretrained weights-can significantly influence downstream performance. Motivated by this observation, we propose ScAle, an ultra-lightweight adaptation method that learns a small set of scalar coefficients to modulate last-token attention and MLP activations in a
The continuous drive to improve the efficiency and performance of large vision language models leads to innovative approaches like ScAle, which aims for significant gains with minimal adaptation.
This development proposes a highly efficient method for enhancing spatial reasoning in Vision Language Models, potentially reducing computational costs and democratizing access to more capable AI.
The ability to significantly improve VLM performance with ultra-lightweight adaptation changes the paradigm from extensive fine-tuning to more resource-efficient scalar adjustments.
- · AI researchers
- · Developers of vision language models
- · Companies with limited compute resources
- · Edge AI applications
- · Developers reliant on costly full fine-tuning
- · Inefficient model architectures
VLMs become more adept at spatial understanding tasks with reduced training overhead.
Broader adoption and deployment of advanced VLMs in resource-constrained environments become feasible.
New classes of AI applications requiring nuanced spatial reasoning on edge devices emerge, accelerating progress in robotics and autonomous systems.
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Read at arXiv cs.LG