WALL-E 2.0: World Alignment by NeuroSymbolic Learning improves World Model-based LLM Agents (2504.15785v1)

Abstract: Can we build accurate world models out of LLMs? How can world models benefit LLM agents? The gap between the prior knowledge of LLMs and the specified environment's dynamics usually bottlenecks LLMs' performance as world models. To bridge the gap, we propose a training-free "world alignment" that learns an environment's symbolic knowledge complementary to LLMs. The symbolic knowledge covers action rules, knowledge graphs, and scene graphs, which are extracted by LLMs from exploration trajectories and encoded into executable codes to regulate LLM agents' policies. We further propose an RL-free, model-based agent "WALL-E 2.0" through the model-predictive control (MPC) framework. Unlike classical MPC requiring costly optimization on the fly, we adopt an LLM agent as an efficient look-ahead optimizer of future steps' actions by interacting with the neurosymbolic world model. While the LLM agent's strong heuristics make it an efficient planner in MPC, the quality of its planned actions is also secured by the accurate predictions of the aligned world model. They together considerably improve learning efficiency in a new environment. On open-world challenges in Mars (Minecraft like) and ALFWorld (embodied indoor environments), WALL-E 2.0 significantly outperforms existing methods, e.g., surpassing baselines in Mars by 16.1%-51.6% of success rate and by at least 61.7% in score. In ALFWorld, it achieves a new record 98% success rate after only 4 iterations.

WALL-E 2.0: Advancements in World Alignment for LLM-Based Agents

The paper "WALL-E 2.0: World Alignment by NeuroSymbolic Learning Improves World Model-based LLM Agents" investigates the integration of LLMs into world models for LLM-based agents. The focus is on addressing the discrepancies between the inherent knowledge of LLMs and the specific dynamics of varied environments, which traditionally limit the performance of LLMs when utilized as world models. This research introduces a training-free method called "world alignment" that extracts symbolic knowledge, including rules, knowledge graphs, and scene graphs, from exploration trajectories. This knowledge is encoded into executable code, guiding LLM agents' decisions.

The paper posits that aligning an LLM with the environment's mechanics significantly enhances its competency as a world model, allowing for more effective embodiment in tasks with complex state spaces like partially observable Markov decision processes (POMDPs). The neuro-symbolic approach effectively models the environment by combining the probabilistic reasoning capabilities of LLMs with the deterministic constraints offered by symbolic representations.

Key Functionalities and Methodology

The paper introduces the following key innovations:

1. Symbolic Knowledge Extraction: Involves extracting actionable rules from the environment as the agent explores. This involves creating actionable plans based on formal rule-based logic which is structured as code, ensuring the reproducibility of the agent’s decisions within environment dynamics.
2. Model-Predictive Control (MPC): The proposed LLM-based MPC framework uses the LLM agent to forecast future actions over an optimized planning horizon. Unlike traditional MPC, which often involves resource-intensive on-the-fly optimization, the LLM's strong heuristics enable it to act as an effective planner with simplified computational demands.
3. Performance Evaluation: The evaluation on benchmarks such as Mars and ALFWorld shows significant performance enhancements over existing baselines. The metrics include improvement in success rates, notably achieving a 98% success rate in ALFWorld, and enhancing performance by margins up to 51.6% on the Mars platform.

Results and Implications

The results demonstrate the efficacy of aligning LLMs with environmental dynamics. Experimentation in diverse conditions revealed that the model-based agent outperformed both reinforcement learning (RL)-based agents which required extensive training periods and LLM-based methods relying heavily on pre-existing knowledge for decision-making tasks.

The incorporation of symbolic reasoning provides stronger guarantees for correct decision-making and safety, making the approach robust in dynamically changing environments. Symbolic elements act as a disciplinary context within which the LLM can frame its reasoning, thereby reducing errors associated with probabilistic predictions.

Future Prospects and Challenges

Future investigations could explore more advanced symbolic reasoning methods to accommodate increasingly complex environments, both stochastic and deterministic, and include richer, more abstract rule representations. Moreover, expanding this framework to incorporate multiple sensory modalities and real-time feedback loops holds potential to further bridge the cognitive flexibility of LLMs with the deterministic reliability of symbolic models.

While promising, the current framework operates under the assumption of deterministic rule execution, which may limit its application in environments that exhibit high degrees of randomness. Thus, considering probabilistic rule execution models could broaden the application scope of neuro-symbolic learning in AI agents.

Overall, this research underscores a significant step towards more intelligent and adaptive AI agents, capable of operating effectively in nuanced and evolving settings, facilitated by the alignment of inherent LLM knowledge with explicit environmental dynamics.