  We propose a novel approach for training large language models (LLMs) to
adhere to objectives defined within a latent embedding space. Our method
leverages reinforcement learning (RL), treating a pre-trained LLM as an
environment. Our embedding-aligned guided language (EAGLE) agent is trained to
iteratively steer the LLM's generation towards optimal regions of the latent
embedding space, w.r.t. some predefined criterion. We demonstrate the
effectiveness of the EAGLE agent using the MovieLens 25M dataset to surface
content gaps that satisfy latent user demand. We also demonstrate the benefit
of using an optimal design of a state-dependent action set to improve EAGLE's
efficiency. Our work paves the way for controlled and grounded text generation
using LLMs, ensuring consistency with domain-specific knowledge and data
representations.
