Can Quantization Actually Help Large Vision-Language Models Instead of Just Shrinking Them?

Post-training quantization makes large vision-language models smaller and faster by reducing numerical precision. A new framework called Balanced Fitting shows that the errors introduced during this process can sometimes improve how models generalize to new tasks.

The Problem With Copying Full Precision

Post-training quantization enables efficient deployment of large vision-language models. The standard approach calibrates on a small dataset while expecting the compressed model to generalize across diverse downstream tasks. Recent methods incorporate sensitivity signals to guide the process. They still minimize reconstruction loss with respect to the full-precision model. This strategy potentially over-preserves FP behavior and calibration-specific bias.

Quantization gets treated solely as an error to be minimized.

The paper notes that over-preserving full-precision behavior can lock in biases from the tiny calibration set. When the quantized model encounters tasks far from that calibration data, the preserved artifacts hurt more than the precision loss would. The authors argue this explains why lower reconstruction loss does not reliably translate into better downstream performance.

Measuring Where Quantization Helps and Where It Hurts

The authors observe that quantization can also provide beneficial regularization for certain layers and modalities.

Motivated by this observation they propose Balanced Fitting, a quantization effect-based framework that balances precision and regularization beyond reconstruction-based optimization. The framework measures layer- and component-wise quantization effects for weights, vision activations, and text activations. It combines fine-grained fitting for sensitive components with coarser fitting to exploit potential regularization benefits.

This goes beyond reconstruction-based optimization.

Balanced Fitting In Practice

The method evaluates each component individually.

Sensitive parts receive precise fitting to preserve critical behavior. Other parts receive coarser fitting that allows regularization effects to emerge. The balance shifts depending on layer type and modality. Vision activations may tolerate different treatment than text activations. Weight quantization follows its own sensitivity profile. The framework adapts automatically rather than applying uniform pressure.

What The Experiments Show

Experiments on multiple LVLMs show that Balanced Fitting consistently outperforms prior PTQ approaches. This holds under both weight-only and weight-activation quantization. Lower reconstruction loss does not reliably translate into better downstream performance.

The source code is publicly available.

How many models were tested and which specific architectures were used the document does not specify. The paper was submitted on 28 September 2026 by Minchan Kang, Kyeonghye Park, Seungyeon Sa, Seoyoung Cho, Daeshik Kim, and Yucheol Cho.

Code Availability And Next Steps

The implementation has been released for community use.

Researchers can reproduce the results and extend the framework. Whether the approach scales to models beyond those tested remains an open question.