Zero-Shot Goal-Directed Dialogue via RL on Imagined Conversations
Joey Hong Sergey Levine Anca Dragan
Large language models (LLMs) have emerged as powerful and general solutions
to many natural language tasks. However, many of the most important
applications of language generation are interactive, where an agent has to talk
to a person to reach a desired outcome. For example, a teacher might try to
understand their student's current comprehension level to tailor their
instruction accordingly, and a travel agent might ask questions of their
customer to understand their preferences in order to recommend activities they
might enjoy. LLMs trained with supervised fine-tuning or "single-step" RL, as
with standard RLHF, might struggle which tasks that require such goal-directed
behavior, since they are not trained to optimize for overall conversational
outcomes after multiple turns of interaction. In this work, we explore a new
method for adapting LLMs with RL for such goal-directed dialogue. Our key
insight is that, though LLMs might not effectively solve goal-directed dialogue
tasks out of the box, they can provide useful data for solving such tasks by
simulating suboptimal but human-like behaviors. Given a textual description of
a goal-directed dialogue task, we leverage LLMs to sample diverse synthetic
rollouts of hypothetical in-domain human-human interactions. Our algorithm then
utilizes this dataset with offline reinforcement learning to train an
interactive conversational agent that can optimize goal-directed objectives
over multiple turns. In effect, the LLM produces examples of possible
interactions, and RL then processes these examples to learn to perform more
optimal interactions. Empirically, we show that our proposed approach achieves
state-of-the-art performance in various goal-directed dialogue tasks that
include teaching and preference elicitation.