Technology Category
Machine Learning: Large Multimodal Models (LMMs)Search and Optimization: Learning to SearchNatural Language Processing: Learning & Optimization for NLP
CoMMIT: Coordinated Instruction Tuning for Multimodal Large Language Models
🤖 AI Summary
To address slow convergence and suboptimal performance in multimodal large language model (MLLM) instruction tuning—caused by imbalanced learning between the language backbone and feature encoders—this paper proposes a collaborative optimization framework. Methodologically, it introduces: (1) a novel learning balance quantification metric that explicitly models gradient dynamics of both components; (2) a state-aware generative distribution regularization loss to enhance cross-modal semantic alignment; and (3) a dynamic learning rate scheduler with a model-agnostic architecture, supporting instruction tuning across visual and audio modalities. Evaluated across diverse LLM backbones and multimodal encoders, the framework significantly accelerates training convergence and achieves state-of-the-art performance on both vision and audio understanding benchmarks.
Technology Category
Application Category
📝 Abstract
Instruction tuning in multimodal large language models (MLLMs) aims to smoothly integrate a backbone LLM with a pre-trained feature encoder for downstream tasks. The major challenge is how to efficiently find the synergy through cooperative learning where LLMs adapt their reasoning abilities in downstream tasks while feature encoders adjust their encoding to provide more relevant modal information. In this paper, we analyze the MLLM instruction tuning from both theoretical and empirical perspectives, where we find unbalanced learning between the two components, i.e., the feature encoder and the LLM, can cause diminishing learning gradients that slow the model convergence and often lead to sub-optimal results due to insufficient learning. Inspired by our findings, we propose a measurement to quantitatively evaluate the learning balance, based on which we further design a dynamic learning scheduler that better coordinates the learning. In addition, we introduce an auxiliary loss regularization method to promote updating of the generation distribution of MLLMs considering the learning state of each model component, which potentially prevents each component from gradient diminishing and enables a more accurate estimation of the learning balance coefficient. We conduct experiments with multiple LLM backbones and feature encoders, where our techniques are model-agnostic and can be generically integrated with various MLLM backbones. Experiment results on multiple downstream tasks and modalities in vision and audio, demonstrate the proposed method's better efficiency and effectiveness in MLLM instruction tuning.
Problem
Research questions and friction points this paper is trying to address.
Addresses unbalanced learning in multimodal instruction tuning
Mitigates oscillation and biased learning for better convergence
Coordinates LLM and feature encoder learning dynamically
Innovation
Methods, ideas, or system contributions that make the work stand out.
Dynamic learning scheduler coordinates LLM and encoder training
Multimodal Balance Coefficient quantitatively measures learning balance
Auxiliary gradient regularization enables larger step size updates
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