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

Hyperparameter Optimization Benchmark: Why It Still Matters

In every modern machine‑learning pipeline, the choice of hyperparameters can mean the difference between a model that barely beats random guessing and one that outperforms the state of the art. From learning rates and batch sizes to architectural choices like the number of layers or attention heads, hyperparameters shape the training dynamics and final performance. Consequently, hyperparameter optimization remains a critical bottleneck in deploying robust models at scale.

Traditional automated hyperparameter search methods—grid search, random search, Bayesian optimisation, evolutionary strategies—have matured into reliable tools. Yet they often treat each trial as an independent experiment, ignoring the rich temporal information that emerges during training. In practice, data scientists spend a disproportionate amount of time manually inspecting training logs, diagnosing over‑fitting, and iteratively tweaking settings. Automating this sequential decision process is the next frontier.

Enter large language models (LLMs). Trained on vast corpora of code, documentation, and natural language, LLMs can read logs, reason about performance trends, and generate new code snippets. When wrapped in an autonomous agent, an LLM can act as a sequential hyperparameter optimizer, proposing new configurations based on the entire history of experiments. To evaluate such agents fairly, the community needed a dedicated benchmark—hence AgentHPOBench.

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The Rise of LLM Agents in Machine‑Learning Pipelines

Large language models have already disrupted natural‑language processing, but their impact on software engineering is equally profound. Code generation, bug fixing, and documentation are now routine tasks for LLMs. In the context of machine learning, an LLM can:

1. Parse training logs to extract metrics, loss curves, and error messages.
2. Infer patterns such as learning‑rate decay schedules or over‑fitting signals.
3. Generate new hyperparameter configurations that balance exploration and exploitation.
4. Produce code to modify training scripts on the fly.

These capabilities suggest that LLM agents could replace or augment traditional hyperparameter optimization tools. However, the field lacks a standardized way to measure how well an LLM agent performs sequentially—that is, how it learns from past trials and improves over time. AgentHPOBench fills this gap by providing a reproducible, diverse set of tasks and a clear evaluation protocol.

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Introducing AgentHPOBench

Benchmark Goals and Design Philosophy

AgentHPOBench is the first benchmark explicitly designed to evaluate LLM agents as sequential hyperparameter optimizers. Its core objectives are:

• Realism – Tasks mirror real‑world experimentation workflows, with a baseline run followed by a fixed number of interventions.
• Diversity – 30 executable ML scripts spanning image classification, natural language processing, time‑series forecasting, and more.
• Transparency – All code, data, and Docker images are open source, ensuring reproducibility.
• Comparability – A standardized interface allows any agent—LLM‑based or traditional—to submit proposals and receive logs.

Task Suite and Search Spaces

Each task in AgentHPOBench comes with:

• A baseline configuration that establishes a performance floor.
• A search space defined in JSON, specifying ranges, categorical options, and constraints.
• A validation metric (e.g., accuracy, F1‑score, RMSE) that the agent seeks to improve.

The search spaces are carefully curated to reflect realistic constraints. For example, the image classification task on CIFAR‑10 might allow learning rates in \([1e-5, 1e-1]\), batch sizes in \([32, 256]\), and the number of convolutional layers in \([2, 6]\).

Sequential Loop Mechanics

The benchmark operates in a sequential loop:

1. Baseline Run – The task is executed once with the baseline configuration. The resulting logs and metrics are stored.
2. Intervention Loop – For a fixed horizon (e.g., 10 steps):
3. The agent receives the cumulative log history.
4. It proposes a new hyperparameter configuration via its propose() method.
5. The runner executes the configuration, appends the new results, and passes the updated history back to the agent.

The evaluation metric is the best validation metric achieved across all interventions, normalized against the baseline. This setup forces the agent to balance short‑term gains with long‑term strategy, mirroring real‑world experimentation.

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Architecture of AgentHPOBench

Core Components

Component	Description
tasks/	30 self‑contained ML scripts (PyTorch, TensorFlow, scikit‑learn).
configs/	JSON files specifying hyper‑parameter search spaces and baseline seeds.
runner.py	Orchestrates the sequential loop, invoking the agent’s propose() method and executing the resulting configuration.
log_parser.py	Normalises logs into a structured format (metrics dictionary, error messages).
evaluation.py	Computes performance metrics, normalises across tasks, and aggregates results.

The repository is hosted on GitHub (OpenMOSS/AgentHPOBench) and includes Docker images for reproducibility.

Agent Interface

Any agent must implement a simple Python interface:

class Agent:
    def __init__(self, task_name: str, search_space: dict):
        """
        Initialise the agent with the task name and search space.
        """
        pass

    def propose(self, history: List[Dict]) -> Dict:
        """
        Given the cumulative history of experiments, return a new hyperparameter
        configuration as a dictionary.
        """
        pass

The history list contains dictionaries with keys such as config, metrics, and logs. Agents can store internal state across calls, enabling sophisticated reasoning.

Execution Flow (Sample Runner)

Below is a minimal excerpt from runner.py that demonstrates the sequential loop:

import json
import subprocess
from typing import List, Dict

def run_task(task_script: str, config: Dict) -> Dict:
    """Execute the task script with the given hyperparameters."""
    cmd = ["python", task_script] + [f"--{k}={v}" for k, v in config.items()]
    result = subprocess.run(cmd, capture_output=True, text=True)
    return parse_logs(result.stdout)

def parse_logs(log_text: str) -> Dict:
    """Parse stdout into a metrics dictionary."""
    metrics = {}
    for line in log_text.splitlines():
        if line.startswith("Metric:"):
            key, val = line.split(":")[1].split("=")
            metrics[key.strip()] = float(val.strip())
    return metrics

def main():
    task_name = "cifar10_resnet"
    baseline_config = json.load(open(f"configs/{task_name}_baseline.json"))
    search_space = json.load(open(f"configs/{task_name}_space.json"))

    agent = Agent(task_name, search_space)

    history = []
    # Baseline run
    baseline_metrics = run_task(f"tasks/{task_name}.py", baseline_config)
    history.append({"config": baseline_config, "metrics": baseline_metrics, "logs": ""})

    # Sequential interventions
    for step in range(10):
        proposal = agent.propose(history)
        metrics = run_task(f"tasks/{task_name}.py", proposal)
        history.append({"config": proposal, "metrics": metrics, "logs": ""})

    # Evaluation
    best_metric = max(h["metrics"]["accuracy"] for h in history)
    print(f"Best accuracy: {best_metric:.4f}")

if __name__ == "__main__":
    main()

This skeleton can be extended with real log parsing, error handling, and parallel execution.

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Evaluating Agents: Metrics and Baselines

Performance Metrics

AgentHPOBench evaluates agents on two primary axes:

1. Best Validation Metric – The highest metric achieved across all interventions.
2. Convergence Speed – How quickly the agent reaches near‑optimal performance (e.g., within 1 % of the best metric).

Both metrics are normalised against the baseline to account for task difficulty.

Baseline Comparisons

To contextualise LLM agent performance, AgentHPOBench includes several conventional HPO baselines:

Baseline	Method	Key Hyperparameters
Random	Random Search	10 000 trials
Bayesian	Tree‑structured Parzen Estimator (TPE)	200 trials
Evolutionary	CMA‑ES	200 trials
LLM Agent	GPT‑4‑Turbo	10 interventions

These baselines provide a spectrum of exploration strategies, from brute‑force to model‑guided search.

Example Results Table

Task	Baseline	Random	Bayesian	Evolutionary	LLM Agent
CIFAR‑10	75.2 %	78.4 %	80.1 %	79.7 %	81.3 %
IMDB Sentiment	82.5 %	84.0 %	85.2 %	84.8 %	86.0 %
Electricity Load	0.12 RMSE	0.10	0.09	0.09	0.08
Average

The table demonstrates that, while LLM agents can outperform traditional methods in many cases, they still lag behind when the search space is highly multimodal or when the agent struggles with noisy logs.

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Challenges for LLM Agents in Sequential HPO

Long‑Term Reasoning

Sequential HPO requires an agent to plan over multiple steps. LLMs excel at short‑term text generation but often lack a persistent memory of past decisions. Without a robust state‑management strategy, agents may repeat suboptimal configurations or fail to exploit promising trends.

Log Interpretation

Training logs contain rich signals—learning‑rate decay, gradient norms, over‑fitting indicators—but are noisy and unstructured. LLM agents must parse these logs accurately to inform their next proposal. Current prompt‑engineering techniques can help, but the agent still needs a reliable parser to avoid misinterpretation.

Exploration vs Exploitation

Balancing exploration (trying new configurations) and exploitation (refining promising ones) is a classic dilemma in HPO. LLM agents can generate diverse proposals, but without a principled exploration strategy they may converge prematurely or waste interventions on marginal gains. Integrating Bayesian priors or evolutionary operators into the agent’s policy can mitigate this issue.

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Extending the Benchmark

Adding Custom Tasks

Researchers can extend AgentHPOBench by adding new tasks:

1. Create a new script in tasks/ following the existing conventions.
2. Define a search space in configs/ as a JSON file.
3. Add a baseline run and optionally a reference metric.

The benchmark’s modular design ensures that new tasks are automatically incorporated into the evaluation pipeline.

Plugging in New Agents

Any agent that implements the Agent interface can be plugged in:

python runner.py --agent my_custom_agent.py --task cifar10_resnet

The agent can be a fine‑tuned LLM, a reinforcement‑learning policy, or a hybrid approach.

Docker and Reproducibility

AgentHPOBench ships with Dockerfiles that encapsulate all dependencies. Running the benchmark in a container guarantees identical environments across machines, eliminating the “works on my machine” problem.

docker build -t agenthpo-bench .
docker run --rm -it agenthpo-bench python runner.py --task cifar10_resnet

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Practical Tips for Building LLM Agent Hyperparameter Tuning Pipelines

Prompt Engineering

• Explicit Instructions – Clearly state the task, current best metric, and constraints.
• Structured Output – Ask the LLM to return a JSON object with hyperparameters, reducing parsing errors.
• Few‑Shot Examples – Provide a few past proposals and their outcomes to guide the agent’s reasoning.

State Management

• Persistent Memory – Store the history in a lightweight database (e.g., SQLite) or in‑memory cache.
• Feature Extraction – Convert raw logs into features (e.g., learning‑rate trend, validation loss slope) before feeding them to the LLM.
• Versioning – Keep track of configuration versions to avoid duplicate proposals.

Error Handling

• Graceful Failures – If a configuration crashes, log the error and skip to the next proposal.
• Fallback Strategies – When the LLM fails to produce a valid configuration, fall back to a random or Bayesian proposal.
• Timeouts – Set reasonable execution time limits to prevent runaway training jobs.

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FAQ

What is AgentHPOBench?

AgentHPOBench is a benchmark designed to evaluate large language model agents that perform sequential hyperparameter optimization across diverse machine‑learning tasks.

How does sequential hyperparameter optimization differ from traditional methods?

Sequential HPO treats each trial as a decision that influences future observations, enabling agents to learn from past results and adapt strategies over time, unlike static, independent trials.

Which tasks are included in the benchmark?

The benchmark covers 30 executable ML tasks spanning image classification, NLP, time‑series forecasting, and more, each with a baseline run and a fixed number of intervention steps.

Can I use AgentHPOBench with my own LLM agent?

Yes, the benchmark provides a standardized interface for agents to submit hyperparameter proposals, receive logs, and iterate, making it easy to plug in custom LLM agents.

What insights can I gain from using AgentHPOBench?

You can assess an agent’s long‑term reasoning, log interpretation, and exploration strategies, compare performance against traditional baselines, and identify gaps in current LLM capabilities.

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Conclusion

AgentHPOBench represents a significant step forward in the evaluation of autonomous hyperparameter optimization. By formalising the sequential nature of real‑world experimentation, it exposes the strengths and weaknesses of LLM agents in a controlled, reproducible setting. Early results show that while LLM agents can match or surpass traditional baselines on many tasks, they still face challenges in long‑term reasoning, log parsing, and exploration‑exploitation trade‑offs.

The future of hyperparameter optimization will likely involve hybrid systems that combine the linguistic and reasoning strengths of LLMs with the statistical rigor of Bayesian methods or the evolutionary robustness of population‑based search. AgentHPOBench will serve as the yardstick against which these innovations are measured, driving the community toward more intelligent, efficient, and autonomous machine‑learning pipelines.