Cache-Efficient Posterior Sampling for Reinforcement Learning with LLM-Derived Priors Across Discrete and Continuous Domains (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) are used as action proposers in reinforcement learning (RL) but they are expensive and require prohibitive computational costs. |
| Approach: | They propose a cache-efficient framework for Bayesian RL that leverages large language models as action proposers and optimizes meta-learning based on policy performance to enable efficient inference across text-based games and robotic control tasks. |
| Outcome: | The proposed framework achieves 3.8–4.7 reduction in LLM queries and 4.0–12.0 lower median latencies (85–93ms on consumer hardware) while retaining 96–98% of the uncached policy’s performance. |
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