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MARS addresses the challenge of imbalanced training dynamics in Multimodal Large Language Models by automating the selection of optimal Low-Rank Adaptation (LoRA) ranks. By introducing a dual scaling law framework, this method effectively prunes the search space to align convergence speeds across different modalities, thereby reducing negative interference without manual tuning. This advancement represents a significant step toward more efficient and robust parameter-efficient fine-tuning strategies for complex multimodal systems.
MARS: Harmonizing Multimodal Convergence via Adaptive Rank Search
arXiv:2603.00720v2 Announce Type: replace Abstract: Fine-tuning Multimodal Large Language Models (MLLMs) with parameter-efficient methods like Low-Rank Adaptation (LoRA) is crucial for task adaptation. However, imbalanced training dynamics across modalities often lead to suboptimal accuracy due to negative interference, a challenge typically addressed with inefficient heuristic methods such as manually tuning separate learning rates. To overcome this, we introduce MARS (Multimodal Adaptive Rank Search), an approach to discover optimal rank pairs that balance training dynamics while maximizing performance. Our key innovation, a proposed framework of dual scaling laws, enables this search: one law models module-specific convergence time to prune the search space to candidates with aligned dynamics, while the other predicts final task performance to select the optimal pair from the pruned set. By re-purposing the LoRA rank as a controller for modality-specific convergence speed, MARS outperforms baseline methods and provides a robust, automated strategy for optimizing MLLM fine-tuning.
Abstract:Fine-tuning Multimodal Large Language Models (MLLMs) with parameter-efficient methods like Low-Rank Adaptation (LoRA) is crucial for task adaptation. However, imbalanced training dynamics across modalities often lead to suboptimal accuracy due to negative interference, a challenge typically addressed with inefficient heuristic methods such as manually tuning separate learning rates. To overcome this, we introduce MARS (Multimodal Adaptive Rank Search), an approach to discover optimal rank pairs that balance training dynamics while maximizing performance. Our key innovation, a proposed framework of dual scaling laws, enables this search: one law models module-specific convergence time to prune the search space to candidates with aligned dynamics, while the other predicts final task performance to select the optimal pair from the pruned set. By re-purposing the LoRA rank as a controller for modality-specific convergence speed, MARS outperforms baseline methods and provides a robust, automated strategy for optimizing MLLM fine-tuning.
Machine Learning (cs.LG)
arXiv:2603.00720 [cs.LG]
(or arXiv:2603.00720v2 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2603.00720 Focus to learn more arXiv-issued DOI via DataCite
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