arXiv:2605.30719v1 Announce Type: new Abstract: We study when large language models (LLMs) can serve as effective black-box policy optimizers for reinforcement learning (RL) tasks, i.e., when can we replace classical RL algorithms with an LLM? We explore this question by introducing Prompted Policy Optimization (PromptPO), an iterative method that prompts an LLM with Python descriptions of the state space, action space, and reward function, then has it generate and refine executable policies based on rollout feedback. Across hard exploration environments, Meta-World robotics tasks, and several
The rapid advancement and increased capabilities of large language models are prompting researchers to explore their utility beyond traditional NLP tasks, especially in areas like complex decision-making and optimization.
This research suggests a potential paradigm shift in how reinforcement learning tasks are approached, allowing LLMs to directly serve as policy optimizers and collapsing the need for separate traditional RL algorithms.
Traditional reinforcement learning algorithms might be progressively replaced or enhanced by LLM-based approaches for policy optimization, particularly for complex and hard exploration scenarios.
- · LLM developers
- · AI agents developers
- · Robotics companies utilizing RL
- · Researchers in reinforcement learning
- · Developers of legacy RL algorithms
- · Companies reliant solely on traditional RL expertise
LLMs become core components of autonomous decision-making systems in various domains, from robotics to industrial control.
The demand for specialized RL expertise might shift towards expertise in prompt engineering and LLM integration for policy optimization.
The abstraction of RL environments for LLM understanding could lead to more generalized AI, where a single LLM can adapt to a wider array of sequential decision-making tasks.
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Read at arXiv cs.LG