MARS: Model-Adaptive Reusable Task-Specific Data Selection for Instruction Tuning

Task-specific instruction data selection aims to select a compact subset that improves performance on a target task. Different base models may benefit from different subsets for the same target task, motivating model-specific adaptation. Existing methods that rely on model-dependent candidate signals typically require recomputing these signals over the full candidate pool when the base model changes, incurring substantial repeated computation across models. Directly reusing a previously selected subset avoids this cost but cannot account for differences across base models. We propose MARS, a model-adaptive and reusable framework that combines reusable candidate characterization with lightweight reference-based model calibration. A fixed external LLM scores each candidate across six ability dimensions to construct a reusable Candidate Ability Atlas. For each target task and base model, MARS combines model feedback from a small target reference set with reference ability distributions to derive model-specific ability allocation and reference profiles. The allocation determines ability-specific quotas, while the profiles guide within-bucket matching. Adapting to a new base model therefore requires evaluation only on the target reference set. Using only 5% of the candidate pool, MARS achieves higher average performance than all compared selection methods and full-data training on every base model across four base models and five target tasks. It also reduces the estimated cumulative selection cost by up to 95.6% compared with existing model-dependent methods. Ablation studies demonstrate the contributions of task-level ability demand, model-specific difficulty, and within-bucket matching. The code is available at https://anonymous.4open.science/r/MARS-0803/.