AI BriefingCan LLMs Beat Traditional Hyperparameter Optimization (HPO) Algorithms
Key point
LLMs proved that hyperparameter optimization performance is maximized when combined with traditional algorithms rather than used alone.
Details
Using the autoresearch framework, in which an LLM agent directly modifies training code to optimize hyperparameters, a comparative study was conducted between LLM-based methods and traditional HPO algorithms.
The results showed that within a fixed search space, traditional methods such as CMA-ES and TPE consistently outperformed LLM-based agents. LLMs struggled to track the optimization state, while traditional methods lacked the domain knowledge that LLMs possess.
To address this, a hybrid approach called Centaur was proposed, with the following features:
- It shares CMA-ES's internal state (mean vector, step size, covariance matrix, etc.) with the LLM
- Even a small 0.8B-scale LLM achieves performance superior to all traditional or pure LLM methods
- LLMs are most effective not when replacing optimization algorithms, but when playing a complementary role
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