Not All Error Yields to Scale: Where Scaling Stops in Vision-Language Inference
Vision-language models (VLMs) face a fixed-budget trade-off between processing more visual information for fine-grained perception and using a larger language backbone for complex reasoning. Existing studies do not tell us which combination of backbone size and input resolution to deploy, especially in high-resolution deployments. To address this gap, we propose the Separable Law that describes how VLM performance changes with language backbone size and visual token count. We fit the law to measurements from 26 InternVL and QwenVL models, with language backbone sizes from 1B to 72B, on four high-resolution benchmarks with image sizes from 224 pixels to 8K. We find that the questions respo...
| Authors | Xinye Zhao, Yunkai Dang, Yunchen Wu et al. |
|---|---|
| Primary category | cs.CV |
| Published | 1 October 2026 |
| Last updated at source | 1 October 2026 |
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