Poe-World+ Planners beat reinforcement learning RL benchmarks in Montezuma’s revenge and use minimal demo data

The importance of symbolic reasoning in world modeling

Understanding how the world works is key to creating AI agents that can adapt to complex situations. Neural network-based models such as Dreamer are flexible, but they require a lot of data to learn effectively, far more than what humans usually do. On the other hand, newer methods combine program synthesis with large language models to generate code-based world models. These are more efficient and can be summarized from limited inputs. However, their use is limited primarily to simple domains, such as text or grid worlds, as scaling to complex, dynamic environments remains a challenge as it is difficult to generate large, comprehensive programs.

Limitations of existing programming world models

Recent research investigates the use of programs to represent the use of world models, often using large language models to synthesize Python transition capabilities. Methods such as WorldCoder and CodeWorldModels generate a large program that limits their scalability in complex environments and their ability to deal with uncertainty and partial observability. Some studies have focused on advanced symbolic models for robotic planning by integrating visual input with abstract reasoning. Earlier efforts adopted restricted domain-specific languages ​​tailored to specific benchmarks or conceptually relevant structures (such as factor graphs in pattern networks). Theoretical models (such as AIXI) also explore world modeling using Turing machines and historical records-based representations.

Introduction to POE World: Modular and Probability World Models

Researchers at Cornell, Cambridge, the Allen Turing Institute and Dalhousie University introduced a specific environmental rule by combining many small, LLM synthesis programs, introducing Poe-world, a method of learning symbolic world models. Instead of creating a large program, Poe-World builds a modular probability structure that can be learned from a brief demonstration. This setup supports a summary of the new situation, and can be effectively planned even in complex games like Pon and Montezuma’s Revenge. Although it does not model raw pixel data, it can learn from symbolic object observations and emphasize accurate modeling rather than exploration for effective decision making.

Architecture and learning mechanisms in the POE world

Poe-world models the environment as a combination of small, interpretable Python programs called “programming experts”, each plan responsible for a specific rule or behavior. These experts are weighted and merged to predict future countries based on past observations and actions. By treating features as conditionally independent and learning from a complete history, the model remains modular and scalable. Hard constraints perfect the predictions and as new data is collected, experts update or prune. This model supports planning and reinforcement learning by simulating possible future results, enabling effective decision-making. The LLMS synthesis program was used and interpreted probabilistically, and the expert weights were optimized by gradient descent.

Atari game experience evaluation

The study evaluated their agents Poe-World + Planner, revenge on Atari’s Pong and Montezuma, including a tougher, modified version of these games. Using minimal demo data, its approach outperforms baselines like PPO, React, and WorldCoder, especially in low data settings. Poe-world demonstrates powerful generalizations by accurately modeling game dynamics, even in environments where changes are made without new demos. It is also the only way to continue to get a positive rating in Montezuma’s revenge. Pre-training policies in POE-World’s simulated environment accelerate real-world learning. Unlike WorldCoder’s limited model, Poe-World produces more detailed representations of constraint awareness, enabling better planning and more realistic behavior in the game.

Conclusion: Symbols for scalable AI plans, modular programs

In short, understanding how the world works is crucial to building adaptive AI agents. However, traditional deep learning models require large data sets and are difficult to update with limited inputs flexibly. This study was inspired by how humans and symbolic systems reorganize knowledge and proposed Poe-world. This method utilizes large language models to synthesize modular programming “experts” representing different regions of the world. These experts combine in composition to form a symbolic, interpretable world model that supports powerful generalizations in the smallest data. Tested in Atari games like Table Tennis and Revenge of Montezuma, this approach proves effective planning and performance even when it is unfamiliar. The code and demo are publicly available.

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