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

Analysis

Why This Paper Matters

As LLMs evolve from code completion systems into autonomous scientific agents, evaluating their ability to conduct experiments becomes crucial. Existing benchmarks focus on static code generation, paper replication, or final answer correctness, but they do not directly assess whether agents can interpret experimental evidence and use it to guide subsequent hyperparameter decisions. AgentHPOBench addresses this gap by introducing a sequential benchmark that requires agents to iteratively propose hyperparameter configurations based on observed results.

This benchmark is significant because it moves beyond single-shot tasks and evaluates the core scientific process of hypothesis-driven experimentation. By providing a unified protocol and a diverse set of tasks, it enables fair comparisons across different agents and conventional HPO methods, highlighting the current state and limitations of LLM agents in this domain.

Technical Contributions

• Sequential Benchmark Design: AgentHPOBench comprises 30 executable ML tasks across seven research categories, each starting with a validated baseline run. Agents perform several sequential interventions, observing accumulated configurations, metrics, and logs before proposing the next valid configuration.
• Unified Evaluation Protocol: The benchmark provides a standardized protocol to evaluate 12 widely used agents and conventional HPO baselines, ensuring consistent and comparable results.
• Focus on Evidence Interpretation: Unlike static benchmarks, AgentHPOBench directly assesses whether agents can interpret experimental evidence (logs, metrics) and use it to guide subsequent decisions.
• Comprehensive Agent Evaluation: The paper evaluates a diverse set of agents, providing insights into their strengths and weaknesses in iterative optimization.

Results

The evaluation of 12 agents and conventional HPO baselines under the unified protocol reveals that current agents exhibit measurable experimental optimization ability across domains. However, they still face clear limitations in sustained iterative refinement, complex log diagnosis, and consistent progress toward reported reference performance. These findings indicate that while LLM agents can perform basic HPO steps, they struggle with long-horizon tasks and nuanced interpretation of experimental logs.

Significance

AgentHPOBench provides a standardized benchmark for assessing LLM agents as autonomous experimentalists, filling a critical gap in the evaluation of scientific agents. It offers a foundation for future research to improve agent capabilities in iterative decision-making and log interpretation. The benchmark's design and findings will likely influence the development of more robust agents for automated machine learning and scientific discovery, potentially accelerating research in various fields.