AdaSparse-PRL: Progressive Reinforcement Learning Fine-tuning with Adaptive Sparse-Aware for Multimodal LLM

Multimodal Large Language Models (MLLMs) excel in cross-modal understanding, fine-tuning is inefficient due to the interdependence of heterogeneous visual and textual features within a shared latent space. To address these issues, we propose AdaSparse-PRL, a progressive reinforcement learning fine-tuning framework with adaptive sparse awareness for MLLMs. Specifically, we first introduce a fine-grained implicit cross-modal decoupling mechanism, which separates latent visual and textual representations at a finer semantic level and reduces interference among heterogeneous modalities during multimodal alignment. Then, an adaptive fine-tuning strategy with dynamic sparsity awareness is designed to identify task-relevant parameters and modal-sensitive activation patterns, thereby eliminating redundant computations and improving training efficiency. Finally, we develop a progressive reinforcement learning method with preference alignment, where the model is optimized through staged reward guidance and gradually aligned with human preferences, thereby improving multimodal instruction-following performance. Extensive experiments on multimodal benchmarks demonstrate that AdaSparse-PRL can increase training speed by more than 1.6x while maintaining accuracy, and reduce computing and memory overhead by more than 30%. Our code and models will be publicly available.