MergeMix: A Unified Augmentation Paradigm for Visual and Multi-Modal
  Understanding
Vision-language alignment in multi-modal large language models (MLLMs)
typically relies on supervised fine-tuning (SFT) or reinforcement learning
(RL). SFT is stable and efficient but requires large-scale human annotations
and cannot capture subtle preferences, while RL brings in a reward signal for
training, but suffers from overhead and instability. These limitations
highlight a trade-off between scalability, robustness, and alignment quality.
To address this, we propose MergeMix, a training-time augmentation paradigm
that bridges SFT and RL. It first applies an attention-aware image mixing via
token merge with more cluster representation and spatial context, and then
presents a preference-driven training paradigm for MLLMs by building preference
pairs with mixed images and raw images, and optimizing via SimPO loss. As a
mixup augmentation, MergeMix enhances attention consistency and efficiency,
surpassing other heuristic-based methods in classification. Extensive
experiments demonstrate that MergeMix achieves competitive accuracy with
improved efficiency, providing a scalable approach to preference alignment in
classification and MLLMs.
6900f489646208eac0d1f0a2
Xin Jin
6900f489646208eac0d1f0a3
Siyuan Li
claimed_verified
2025-10-29T12:49:15.026Z
640f7083208821a59b74c757
https://cdn-avatars.huggingface.co/v1/production/uploads/1678735253848-640f7083208821a59b74c757.jpeg
Siyuan Li
Lupin1998
user
Lupin1998
6900f489646208eac0d1f0a4
Siyong Jian
6900f489646208eac0d1f0a5
Kai Yu
6900f489646208eac0d1f0a6
Huan Wang
MergeMix: A Unified Augmentation Paradigm for Visual and Multi-Modal
  Understanding
Vision-language alignment in multi-modal large language models (MLLMs)
typically relies on supervised fine-tuning (SFT) or reinforcement learning
(RL). SFT is stable and efficient but requires large-scale human annotations
and cannot capture subtle preferences, while RL brings in a reward signal for
training, but suffers from overhead and instability. These limitations
highlight a trade-off between scalability, robustness, and alignment quality.
To address this, we propose MergeMix, a training-time augmentation paradigm
that bridges SFT and RL. It first applies an attention-aware image mixing via
token merge with more cluster representation and spatial context, and then
presents a preference-driven training paradigm for MLLMs by building preference
pairs with mixed images and raw images, and optimizing via SimPO loss. As a
mixup augmentation, MergeMix enhances attention consistency and efficiency,
surpassing other heuristic-based methods in classification. Extensive
experiments demonstrate that MergeMix achieves competitive accuracy with
improved efficiency, providing a scalable approach to preference alignment in
classification and MLLMs.
MergeMix, a training-time augmentation method, combines attention-aware image mixing and preference-driven training to improve vision-language alignment in multi-modal large language models with enhanced efficiency and accuracy.
supervised fine-tuning reinforcement learning attention-aware image mixing token merge cluster representation spatial context preference-driven training preference pairs SimPO loss mixup augmentation attention consistency