ICML 2025

Merge-Friendly Post-Training Quantization for
Multi-Target Domain Adaptation
Juncheol Shin 1 Minsang Seok 2 Seonggon Kim 2 Eunhyeok Park 1 2
Abstract
Model merging has emerged as a powerful tech-
nique for combining task-specific weights, achiev-
ing superior performance in multi-target domain
adaptation. However, when applied to practical
scenarios, such as quantized models, new chal-
lenges arise. In practical scenarios, quantization
is often applied to target-specific data, but this
process restricts the domain of interest and intro-
duces discretization effects, making model merg-
ing highly non-trivial. In this study, we analyze
the impact of quantization on model merging
through the lens of error barriers. Leveraging
these insights, we propose a novel post-training
quantization, HDRQ - Hessian and distant regu-
larizing quantization - that is designed to consider
model merging for multi-target domain adapta-
tion. Our approach ensures that the quantization
process incurs minimal deviation from the source
pre-trained model while flattening the loss surface
to facilitate smooth model merging. To our knowl-
edge, this is the first study on this challenge, and
extensive experiments confirm its effectiveness.
1. Introduction
Large-scale models have driven breakthroughs across vari-
ous domains, particularly in generative AI, enabling efficient
adaptation to multiple tasks and user-specific data. However,
deploying such models on resource-constrained devices re-
mains a significant challenge due to their computational de-
mands. In this perspective, model merging has emerged as a
promising technique that enables multi-target domain adap-
tation without additional training. A recent study on multi-
1Graduate School of Artificial Intelligence, Pohang Univer-
sity of Science and Technology (POSTECH), Pohang, Repub-
lic of Korea 2Department of Computer Science and Engineer-
ing, Pohang University of Science and Technology (POSTECH),
Pohang, Republic of Korea. Correspond...