Can LLMs Beat Classical Hyperparameter Optimization Algorithms?
A benchmark study evaluating whether LLMs can outperform classical hyperparameter optimization algorithms like Bayesian optimization and random search.
This paper investigates whether LLMs can serve as effective hyperparameter optimization (HPO) agents, competing with established classical methods such as Bayesian optimization, TPE, and random search. The study likely employs a systematic evaluation framework where LLMs iteratively suggest hyperparameter configurations based on task descriptions and historical evaluation results. Findings aim to clarify the practical potential and limitations of LLMs in AutoML pipelines.
超參數優化（Hyperparameter Optimization，HPO）是機器學習實踐中最耗時、最資源密集的環節之一。選擇學習率、批次大小、正則化係數、網路層數等超參數，往往需要數十乃至數百次模型訓練才能找到相對最優解。傳統的解決方案包括：網格搜索（Grid Search）、隨機搜索（Random Search）、以貝葉斯推斷為基礎的 Bayesian Optimization（BO）、Tree-structured Parzen Estimator（TPE）、SMAC，以及結合早停策略的 Hyperband 與 BOHB 等演算法，這些方法已在業界和學術界積累了大量的實證驗證。
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