From Understanding Language to Understanding the World: Learning "Balance" with LLM
As large language models (LLMs) such as ChatGPT, Claude, and Gemini evolve rapidly, we are increasingly seeing their applications in tasks like document processing and customer service automation. However, LLMs are not just "text understanding tools", but can be seen as a type of intelligent entity with a "world model".
Through the world model, LLMs not only can answer questions, but also predict physical events, simulate environmental interactions, and control physical devices. To verify whether LLMs have this "world understanding capability", we implemented a classic control task: the "Inverted Pendulum" - which is to keep a vertically standing rod balanced on a movable cart.
The key to this task is precise motor control: the movement of the cart relies on the driving force from the motor at the bottom, and if the pendulum starts to tilt, the motor must generate the appropriate thrust in a timely manner to move the cart to the right position and counteract the torque caused by the swing. In other words, the inverted pendulum is a microcosm of the motor control problem, combining real-time sensing, prediction, and action adjustment.
This is not only an entry-level problem for robot control, but also a suitable example to observe how AI learns to control motors, reason about physical states, and achieve balance.
What is a world model?
A world model refers to an AI's internal understanding and simulation of how the external world operates. Based on experience or observation, it establishes causal relationships (e.g., applying force will cause movement, changes in angle will lead to loss of balance), and then predicts what will happen next and plans the most appropriate actions.
Design Process: Letting AI Learn "Maintaining Balance" through Simulation
To enable a large language model (LLM) or other AI models to understand and control a physical system, we need to transform the "real world" into a structured process that AI can learn. The overall logic can be imagined as follows:
We first establish a virtual world → Then define the physical rules and interaction methods of this world → Let AI repeatedly try and learn in this world → Finally, verify whether it has truly learned how to control the operation of this world.
In this inverted pendulum control experiment, we broke down this learning task into the following four core stages:
1
Establishing the Simulation Environment: Building the Stage for AI Learning
We designed a customized simulation environment from scratch, using the Gym framework as the foundation. This simulated world mimics the real physical scenario: the cart can move horizontally, and a pendulum is mounted on the cart, with the goal of keeping the pendulum upright. In this stage, we clearly defined:
- The length, mass, and moment of inertia of the pendulum
- The speed limit of the cart's movement
- Natural physical constants like gravitational acceleration
- The discrete time step (i.e., the time unit for each simulation step)
These parameters make the simulation system able to realistically reflect the laws of physical motion, becoming the "laboratory" for AI models to interact and learn.
2
Defining Control Logic and Scoring Rules: Letting AI Know What is a "Good Action"
Next, we designed the action control and reward logic, which is to tell the AI: "What kind of actions are considered successful". This part includes:
- How to update the cart's position based on the "thrust" provided by the AI
- How to calculate the angle and acceleration changes of the pendulum using physical formulas
- The closer the pendulum is to vertical (180 degrees), the higher the reward the AI gets; if it deviates too far, points are deducted
- When both the angle and angular velocity are stabilized, the task is considered successful and the episode can be terminated early
These rules are the basis for the AI to judge the "effect of each action", which is the so-called Reward Function, a core design element in reinforcement learning.
3
Training the AI Model: Learning to Control the World through Reinforcement Learning
With the environment and rules in place, we used one of the most common reinforcement learning algorithms today: PPO (Proximal Policy Optimization) to train the AI model.
The training process can be likened to a child learning to walk: the AI constantly tries, fails, and then corrects. The model updates its strategy based on the results of each attempt, so that it can make better decisions when faced with similar situations in the future.
The key parameters we set include:
- Learning rate: Affects the speed and magnitude of model updates.
- Batch size: Determines the number of samples taken in each training round, which relates to the stability of model learning.
- Discount factor (gamma): Reflects the AI's emphasis on future rewards, with values closer to 1 indicating a greater focus on long-term rewards.
Through tens of thousands of simulations and learning, the AI model ultimately stabilized the cart's movement and maintained the pendulum's upright position, achieving the goal of establishing a world model from experience and formulating the optimal control strategy.
4
Testing and Visualization: Verifying if the AI Has Really Learned
Finally, we repeatedly tested the trained model and presented the entire control process in the form of animation.
The dynamic images clearly show:
- How the cart quickly adjusts its position to the left and right to maintain balance
- The pendulum remains stable even in a dynamic environment
- Whether the changes in angle and velocity at each time point are as expected
What is Gym?
Gym is a reinforcement learning simulation environment package developed by OpenAI, which provides standardized "Observation Space", "Action Space", "Reward Function", and "Environment State Transition Logic", allowing researchers to quickly build virtual worlds for AI to interact and train. You can think of Gym as an "AI-exclusive virtual lab" where AI can learn to try, make mistakes, and correct, just like a child, until it masters the control logic.
What is PPO?
PPO is a policy-based reinforcement learning algorithm that can "stably and slightly adjust the behavior policy" during each update, avoiding over-learning that leads to unstable performance. Compared to traditional algorithms, PPO combines learning efficiency and stability, and is widely used in areas like robot control, game AI, and autonomous vehicle simulation. By "limiting the update range", it ensures that each learning step is towards a better direction, but not too far off, similar to a "safe learning method of step-by-step behavior correction".
Enabling Language Models to Not Just Talk, But Also "Take Action"
Although the inverted pendulum control experiment is a simple test scenario, the more important thing is that it demonstrates a significant breakthrough: language models (LLMs) are no longer just dialogue tools, but intelligent hubs that can understand the rules of the world and actively control physical devices.
This process allows us to verify that LLMs are gradually acquiring the following three key capabilities:
- The Ability to Dynamically Understand the World (World Modeling)
Through repeated interactions with the simulation environment, LLMs can master causal logic such as "applying force will cause displacement" and "changes in angle will affect balance", and then predict the future state of the system. This is what is called the world model. AI not only remembers the results, but also understands the process, abstracting a stable and transferable internal model from physical reactions, which also lays a critical foundation for areas like robot control, smart manufacturing, and digital twins.
- The Ability to Translate from Language Reasoning to Physical Control (Language-to-Action)
The real breakthrough of language models lies in their ability to derive executable control strategies from natural language understanding. We no longer need to hard-code every instruction condition, but only need to input a simple command: "Keep the cart's pendulum upright". The LLM can then generate corresponding action plans based on the learned world model, and execute specific actions through the training results of reinforcement learning. This "language-to-action" capability makes language a control interface for driving motors, devices, and equipment in the real world, enabling the translation between semantics and behavior, and ushering in a new era of more natural and intuitive human-machine interaction.
- The Generalization Capability to Adapt to Unknown Environments
Past control systems were often limited to preset scenarios and could not effectively cope with new changes or unexpected situations. However, this experiment demonstrates that LLMs combined with reinforcement learning can still stably control the inverted pendulum under different initial states, showing their strong generalization capability. This means the model is not just "memorizing solutions", but truly learning how to respond optimally based on the current situation. In the future, regardless of different friction, pendulum lengths, or device sizes, these models will be able to quickly adjust their strategies, exhibiting cross-domain and cross-scenario adaptability, which is crucial for actual deployment in factory floors, home appliances, or diverse outdoor robot environments.
Letting Language Drive the World: The Next Step of AI Control
The inverted pendulum control task is just the first step, allowing us to witness the possibility of language models (LLMs) transitioning from text reasoning to action decision-making. When AI can understand the world model, drive control behaviors through language, and have the generalization capability to handle various environmental variables, the definition and boundaries of intelligent systems will be fundamentally rewritten.
In the future, we will no longer need to manually design complex logic flows and control functions. Just through language communication, AI can adjust its strategies based on its understanding of the goal and environment, and complete the entire closed-loop from perception to execution. This will open up a new chapter for smart manufacturing, robot collaboration, smart logistics, autonomous vehicles, and AI assistant devices.
LLM controlling motors is not just "language-driven hardware", but represents AI's ability to intervene in the real world and actively participate in decision-making. Language models will not only be encoders and decoders of information, but gradually become the control brain of the entire system, from receiving instructions, to understanding tasks, and then executing actions, completing the last mile between human language and machine control.
This "language-to-action" capability will not only change the role of AI, but also change the way developers design systems, the standards by which enterprises define intelligence, and the interaction patterns between humans and machines. Now is the best time to start using AI control.