DaMo: Data Mixing Optimizer in Fine-tuning Multimodal LLMs for Mobile Phone Agents

Mobile Phone Agents (MPAs) have emerged as a promising research direction due to their broad applicability across diverse scenarios.While Multimodal Large Language Models (MLLMs) serve as the foundation for MPAs, their effectiveness in handling multiple mobile phone tasks simultaneously remains limited.Although multitask supervised fine-tuning (SFT) is widely adopted for multitask learning, existing approaches struggle to determine optimal training data compositions for peak performance.To address this challenge, we propose DaMo (Data Mixture Optimizer)a novel solution employing a trainable network that predicts optimal data mixtures by forecasting downstream task performance for any given dataset ratio.To support comprehensive evaluation, we introduce PhoneAgent-Bench, the first specialized benchmark to evaluate MLLMs on multimodal mobile phone tasks, comprising 1,235 QA pairs spanning diverse real-world industrial mobile application scenarios.Demonstrating strong predictive capability (R²=0.81) in small-scale pilot experiments, DaMo efficiently extrapolates optimal data mixing configurations.Our results show DaMo achieves 3.06% average score improvement on PhoneAgentBench and open-source benchmarks, including BFCL-v3, MME-Reasoning, MME-Perception, and OCRBench, compared to alternative methods.Through predicting optimal data mixture only on open-source benchmarks, DaMo outperforms other approaches by 6.70% in terms of average score.Moreover, DaMo improves the metrics by 12.74% than other methods when used solely for MLLM optimization on the BFCL-v3 task.Notably, DaMo maintains robust scalability, preserving its effectiveness when applied to other model architectures.