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Hyperparameter optimization (HPO) is crucial for fine-tuning machine learningmodels but can be computationally expensive. To reduce costs, Multi-fidelityHPO (MF-HPO) leverages intermediate accuracy levels in the learning process anddiscards low-performing models early on. We compared various representativeMF-HPO methods against a simple baseline on classical benchmark data. Thebaseline involved discarding all models except the Top-K after training foronly one epoch, followed by further training to select the best model.Surprisingly, this baseline achieved similar results to its counterparts, whilerequiring an order of magnitude less computation. Upon analyzing the learningcurves of the benchmark data, we observed a few dominant learning curves, whichexplained the success of our baseline. This suggests that researchers should(1) always use the suggested baseline in benchmarks and (2) broaden thediversity of MF-HPO benchmarks to include more complex cases.

Is One Epoch All You Need For Multi-Fidelity Hyperparameter Optimization?