AgentHPOBench: Benchmarking LLM Agents as Sequential Hyperparameter Optimizers
A new standard, AgentHPOBench, has been launched to assess how effectively large language model (LLM) agents function as sequential hyperparameter optimizers for machine learning tasks. This benchmark includes 30 executable tasks divided into seven research categories, each beginning with a validated baseline run. Agents execute sequential interventions, analyzing accumulated metrics, configurations, and logs prior to suggesting the next configuration. The research examines 12 commonly utilized agents alongside traditional HPO baselines within a unified framework, uncovering significant performance disparities among current agents. This study fills a void in existing benchmarks, which usually emphasize static code generation or the accuracy of final answers rather than the iterative analysis of experimental data. The paper can be found on arXiv with the identifier 2607.29626.
Key facts
- AgentHPOBench is a sequential benchmark for evaluating LLM agents as hyperparameter optimizers.
- It includes 30 executable machine learning tasks across seven research categories.
- Each task begins with a validated baseline run.
- Agents perform sequential interventions, observing configurations, metrics, and logs.
- 12 widely used agents and conventional HPO baselines are evaluated under a unified protocol.
- Results show current agents exhibit measurable performance gaps.
- The benchmark addresses a gap in existing benchmarks that focus on static code generation or final answer correctness.
- The paper is available on arXiv with identifier 2607.29626.
Entities
Institutions
- arXiv