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AgentHPOBench: A Benchmark For Evaluating LLM Agents as Sequential Hyperparameter Optimizers

Artificial Intelligence Computer Science

AgentHPOBench evaluates LLM agents as sequential hyperparameter optimizers using 30 executable tasks across seven categories. Agents observe accumulated configurations, metrics, and logs to propose valid next steps. Results show measurable optimization ability but highlight limitations in sustained iterative refinement, complex log diagnosis, and consistent progress toward reported reference performance compared to conventional baselines.

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Key takeaways

AgentHPOBench provides a sequential benchmark comprising 30 executable machine learning tasks across seven research categories.
Each task begins with a validated baseline run, after which an agent performs several sequential interventions based on observed evidence.
The benchmark evaluates 12 widely used agents and conventional HPO baselines under a unified protocol.
Current agents exhibit measurable experimental optimization ability across domains, yet face clear limitations in sustained iterative refinement.
Agents struggle with complex log diagnosis and consistent progress toward reported reference performance.
Removing intermediate experimental evidence reduces optimization performance, while later interventions may plateau or discard earlier gains.

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