In this paper, we propose a novel framework that integrates a large-scale language model (LLM) and a reinforcement learning-based dialogue manager for goal-oriented open-ended conversations. By leveraging hierarchical reinforcement learning to model the structural stages of a conversation and meta-learning to enhance its adaptability to different user profiles, we can learn from limited data, transition seamlessly between conversation stages, and personalize responses to heterogeneous user needs. By applying our framework to motivational interviews to promote behavioral change, we demonstrate that the proposed dialogue manager outperforms the state-of-the-art LLM baseline model in terms of rewards, thereby demonstrating the potential benefits of LLM conditioning for generating goal-oriented open-ended conversation systems.
