BRACE: Benefit-aware Reuse and Adaptive Capacity Expansion for Multimodal Continual Instruction Tuning

Multimodal continual instruction tuning requires models to learn new tasks while retaining existing knowledge. Existing methods primarily rely on parameter-level protection or module-level management, facing two limitations: 1) Insufficient historical knowledge protection and reuse, as preserving parameters or modules alone struggles to ensure both stable knowledge retention and accurate selection of beneficial components, potentially causing forgetting, negative transfer, and redundant learning; 2) Inefficient capacity allocation and utilization, as residual demand after reuse and component complementarity are insufficiently considered, leading to imbalanced capacity allocation and directional redundancy. To address these limitations, we propose BRACE, which organizes rank-one LoRA components into independently manageable Atoms. The FixedAtom component freezes historical Atoms and task catalogs and enables selective reuse through benefit-aware selection. The AdaAtom component adjusts the number and layer-wise distribution of new Atoms according to residual demand after reuse under a global capacity constraint. An input diversity loss further reduces directional redundancy among new Atoms within each layer. We evaluate BRACE on UCIT and MLLM-DCL using two multimodal backbones. On LLaVA-1.5, BRACE improves mean final performance over the best prior methods by 6.22 and 8.53 percentage points on the two benchmarks, respectively. Compared with BRACE-NA, a variant without AdaAtom or input diversity loss, BRACE reduces cumulative adaptation parameters by 38.3% and 29.2%, respectively, with comparable performance.