Citation

Chowdhury, Md Tahmid Ashraf and Ullah, Fasee and Hassan, Mohd Hilmi and Bhushan, Shashi and Kamal, Shahid and Khan, Arfat Ahmad (2026) LLM-guided population-based reinforcement learning: A scalable methodology for adaptive hyperparameter optimization. MethodsX, 16. p. 103879. ISSN 2215-0161

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Abstract

Population-Based Training (PBT) has the drawback of using fixed, pre-programmed mutation and selection rules to optimize hyperparameters, which are not always flexible across reinforcement learning (RL) tasks. To address this, we introduce LLM-Guided Population-Based Reinforcement Learning (LPBRL), a scalable methodology in which the reasoning capability of Large Language Models (LLMs) is used to manage population evolution dynamically. LPBRL operates through a six-phase cycle in which the LLM analyzes real-time performance measurements from parallel workers and produces adaptive population-update recommendations as a substitute for static rules. In contrast to conventional PBT, and unlike prior LLM-assisted optimization frameworks that typically operate outside the recurrent population loop, LPBRL places language-model reasoning directly inside the selection-mutation stage of training. This enables task-aware hyperparameter adaptation that improves convergence speed and training stability. We evaluated LPBRL on CartPole-v1 with 8 parallel workers over 150 episodes and observed clear gains over conventional PBT, with best- and average-reward convergence improving by 62.5 percent and 68.2 percent, respectively. Although the approach requires access to LLM APIs and compatible RL tooling such as Stable-Baselines3, the results show strong potential for large-scale training workflows in which adaptive hyperparameter control is essential. Overall, the empirical findings support the claim that language-model reasoning can make effective optimization decisions in RL while preserving the practical strengths of population-based training. • Large Language Models are integrated as adaptive decision-makers inside the recurrent population-evolution loop, replacing static task-agnostic mutation and selection rules with context-aware reasoning.Real-time worker metrics, trajectory trends, and LLM-guided hyperparameter adaptation accelerate convergence and improve stability across discrete and continuous-control RL settings. • The methodology provides a reproducible implementation path with structured prompts, deterministic parsing, bounded updates, and compatibility with multiple RL algorithms (PPO, SAC, TD3), supporting large-scale applications.

Item Type:	Article
Uncontrolled Keywords:	Reinforcement learning, Large language models, Population-based training, Hyperparameter optimization, Adaptive learning
Subjects:	H Social Sciences > HT Communities. Classes. Races > HT51-65 Human settlements. Communities
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	04 May 2026 04:21
Last Modified:	07 May 2026 09:08
URII:	http://shdl.mmu.edu.my/id/eprint/15864

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