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Large Language Model Agent for Hyper-Parameter Optimization

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arxiv 2402.01881 v3 pith:XVV3FVJD submitted 2024-02-02 cs.LG cs.AI

Siyi Liu , Chen Gao , Yong Li This is my paper
classification cs.LGcs.AI
keywords optimizationtrialsagenthpohyperparameterlearningmachinetasksautoml
verification ladder T0 review T1 audit T2 compute T3 formal
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Hyperparameter optimization is critical in modern machine learning, requiring expert knowledge, numerous trials, and high computational and human resources. Despite the advancements in Automated Machine Learning (AutoML), challenges in terms of trial efficiency, setup complexity, and interoperability still persist. To address these issues, we introduce a novel paradigm leveraging Large Language Models (LLMs) to automate hyperparameter optimization across diverse machine learning tasks, which is named AgentHPO (short for LLM Agent-based Hyperparameter Optimization). Specifically, AgentHPO processes the task information autonomously, conducts experiments with specific hyperparameters (HPs), and iteratively optimizes them based on historical trials. This human-like optimization process largely reduces the number of required trials, simplifies the setup process, and enhances interpretability and user trust, compared to traditional AutoML methods. Extensive empirical experiments conducted on 12 representative machine-learning tasks indicate that AgentHPO not only matches but also often surpasses the best human trials in terms of performance while simultaneously providing explainable results. Further analysis sheds light on the strategies employed by the LLM in optimizing these tasks, highlighting its effectiveness and adaptability in various scenarios.

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Cited by 9 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

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  3. Reinforcement Learning for Machine Learning Engineering Agents

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  4. Matryoshka Agent: Unfolding Sub-Agents for Long-Horizon Machine Learning Engineering

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    Matryoshka Agent’s orchestrator–sub-agent hierarchy plus tree-ranked RL raises MLE-Dojo HumanRank, letting a 4B orchestrator approach o4-mini and giving a 30B coder up to 36.7% relative gain.

  5. A Language-Guided Bayesian Optimization for Efficient LoRA Hyperparameter Search

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    LLM embeddings plus Bayesian optimization find better LoRA hyperparameters in ~30 proxy trials than standard published settings.

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  9. Systematic Optimization of Open Source Large Language Models for Mathematical Reasoning

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