WALL-E: World Alignment by Rule Learning Improves World Model-based LLM
  Agents
Authors: name: Siyu Zhou, name: Tianyi Zhou, name: Yijun Yang, name: Guodong Long, name: Deheng Ye, name: Jing Jiang, name: Chengqi Zhang

Can large language models (LLMs) directly serve as powerful world models for
model-based agents? While the gaps between the prior knowledge of LLMs and the
specified environment's dynamics do exist, our study reveals that the gaps can
be bridged by aligning an LLM with its deployed environment and such "world
alignment" can be efficiently achieved by rule learning on LLMs. Given the rich
prior knowledge of LLMs, only a few additional rules suffice to align LLM
predictions with the specified environment dynamics. To this end, we propose a
neurosymbolic approach to learn these rules gradient-free through LLMs, by
inducing, updating, and pruning rules based on comparisons of agent-explored
trajectories and world model predictions. The resulting world model is composed
of the LLM and the learned rules. Our embodied LLM agent "WALL-E" is built upon
model-predictive control (MPC). By optimizing look-ahead actions based on the
precise world model, MPC significantly improves exploration and learning
efficiency. Compared to existing LLM agents, WALL-E's reasoning only requires a
few principal rules rather than verbose buffered trajectories being included in
the LLM input. On open-world challenges in Minecraft and ALFWorld, WALL-E
achieves higher success rates than existing methods, with lower costs on
replanning time and the number of tokens used for reasoning. In Minecraft,
WALL-E exceeds baselines by 15-30% in success rate while costing 8-20 fewer
replanning rounds and only 60-80% of tokens. In ALFWorld, its success rate
surges to a new record high of 95% only after 6 iterations.