<ul><li>A new approach integrating large language models (LLMs) as priors in reinforcement learning (RL) has been introduced.</li><li>The approach presents a cache-efficient framework for posterior sampling with LLM-derived priors, reducing computational costs while maintaining high performance.</li><li>The framework uses an adaptive caching mechanism, resulting in a 3.8--4.7$	imes$ reduction in LLM queries and 4.0--12.0$	imes$ lower median latencies on a consumer GPU.</li><li>Extensive evaluations across multiple tasks show the generalizability and practical viability of LLM-guided RL in resource-constrained settings.</li></ul>

Cache-Efficient Posterior Sampling for Reinforcement Learning with LLM-Derived Priors Across Discrete and Continuous Domains

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