  Reinforcement learning (RL) has become the de facto standard practice for
sequential decision-making problems by improving future acting policies with
feedback. However, RL algorithms may require extensive trial-and-error
interactions to collect useful feedback for improvement. On the other hand,
recent developments in large language models (LLMs) have showcased impressive
capabilities in language understanding and generation, yet they fall short in
exploration and self-improvement capabilities for planning tasks, lacking the
ability to autonomously refine their responses based on feedback. Therefore, in
this paper, we study how the policy prior provided by the LLM can enhance the
sample efficiency of RL algorithms. Specifically, we develop an algorithm named
LINVIT that incorporates LLM guidance as a regularization factor in value-based
RL, leading to significant reductions in the amount of data needed for
learning, particularly when the difference between the ideal policy and the
LLM-informed policy is small, which suggests that the initial policy is close
to optimal, reducing the need for further exploration. Additionally, we present
a practical algorithm SLINVIT that simplifies the construction of the value
function and employs subgoals to reduce the search complexity. Our experiments
across three interactive environments ALFWorld, InterCode, and BlocksWorld
demonstrate that our method achieves state-of-the-art success rates and also
surpasses previous RL and LLM approaches in terms of sample efficiency. Our
code is available at https://github.com/agentification/Language-Integrated-VI.
