WALL-E: World Alignment by Rule Learning Improves World Model-based LLM Agents (2410.07484v2)

Abstract: Can 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.

Overview of "WALL-E: World Alignment by Rule Learning Improves World Model-based LLM Agents"

The paper introduces a novel approach to utilizing LLMs as world models for agents, specifically through a method termed "World Alignment by Rule Learning" (WALL-E). The primary focus is to address the gap between an LLM’s inherent knowledge and the specified dynamics of an environment using a neurosymbolic technique that leverages rule learning. This approach contrasts with existing methods which often rely on fine-tuning or the extensive use of buffered trajectories.

Neurosymbolic Rule Learning

The proposed approach involves a neurosymbolic framework that efficiently aligns an LLM's predictions with environmental dynamics by learning additional rules. These rules are applied to refine the world model without gradient-descent-based updates. The authors employ a gradient-free method to induce, update, and prune rules based on comparisons between real-world trajectories and LLM-predicted outcomes. As a result, the agent is enhanced by a precise world model that integrates LLMs with learned rules.

Model Predictive Control Framework

The paper integrates the LLM-based world model within a model-predictive control (MPC) framework. This integration allows for optimizing actions in a look-ahead manner, significantly improving both exploration and learning efficiency. The agent's reasoning, relying on a few principal rules rather than verbose input, achieves enhanced performance on complex tasks in dynamic environments.

Performance Evaluation

WALL-E was benchmarked against challenges in open-world settings such as Minecraft and ALFWorld. The results are promising, with WALL-E surpassing existing models by 15-30% in success rate in Minecraft while also proving cost-effective in terms of replanning time and tokens used. In ALFWorld, WALL-E reached a new record high success rate of 95% within just six iterations, demonstrating the efficacy of the rule-learning approach over traditional methods.

Implications and Future Directions

The research presents several implications for AI and agent-based modeling. Practically, it highlights the potential of LLMs as dynamic agents when properly aligned through minimal rule adjustments. Theoretically, it suggests a shift towards integrating symbolic reasoning with neural capabilities to achieve world models that are both flexible and robust.

For future work, the exploration of more abstract rule generation and the handling of stochastic environmental dynamics are proposed as promising directions. Given the probabilistic nature of actions in many environments, developing rules that account for stochastic outcomes could further enhance model reliability.

In summary, the paper provides a robust framework that leverages rule learning for efficient world alignment, setting a foundation for developing more capable LLM-based agents.