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.
Hyperparameter Optimization (HPO) is one of the most time-consuming and resource-intensive aspects of machine learning practice. Selecting hyperparameters such as learning rate, batch size, regularization coefficient, and network layer count often requires dozens or even hundreds of model training cycles to find the relatively optimal solution. Traditional solutions include: Grid Search, Random Search, Bayesian Optimization (BO) based on Bayesian inference, Tree-structured Parzen Estimator (TPE), SMAC, as well as algorithms like Hyperband and BOHB that combine early stop strategies. These methods have accumulated extensive empirical validation in both industry and academia.
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