  Contextual bandits have emerged as a cornerstone in reinforcement learning,
enabling systems to make decisions with partial feedback. However, as contexts
grow in complexity, traditional bandit algorithms can face challenges in
adequately capturing and utilizing such contexts. In this paper, we propose a
novel integration of large language models (LLMs) with the contextual bandit
framework. By leveraging LLMs as an encoder, we enrich the representation of
the context, providing the bandit with a denser and more informative view.
Preliminary results on synthetic datasets demonstrate the potential of this
approach, showing notable improvements in cumulative rewards and reductions in
regret compared to traditional bandit algorithms. This integration not only
showcases the capabilities of LLMs in reinforcement learning but also opens the
door to a new era of contextually-aware decision systems.
