Current language model training commonly applies multi-task Supervised Fine-Tuning (SFT) using a homogeneous compute budget across all sub-datasets. This approach is fundamentally sub-optimal: heterogeneous learning dynamics cause faster-learning tasks to overfit early while slower ones remain under-fitted. To address this, we introduce mSFT, an iterative, overfitting-aware search algorithm for multi-task data mixtures. mSFT trains the model on an active mixture, identifies and excludes the earliest overfitting sub-dataset, and reverts to that specific optimal checkpoint before continuing. Extensive evaluations demonstrate that mSFT *consistently* outperforms **4** baselines across **10** benchmarks and **6** base models. Further analysis confirms mSFT maintains robust gains across diverse dataset sizes, task granularities, and is insensitive to its single new hyperparameter, compute budget. Notably, at a low compute budget, mSFT can improve performance *while* lowering training FLOPs. Ultimately, mSFT establishes a *practical* overfitting-aware algorithm for multi-task SFT that maximizes the potential of models across diverse data mixtures.