World Models are models that learn to represent and predict the dynamics of an environment: how the state of the world evolves in response to an agent's actions. Unlike a language model, which predicts the next word, a world model predicts the next situation (for example, the frames of a scene or the physical state of a system), building an internal representation that captures causal and temporal relationships.
Their importance lies in the fact that they allow an agent to plan and imagine consequences before acting, reducing the need for trial and error in the real environment. This is key in:
- Robotics, to anticipate the outcome of a movement.
- Autonomous driving, simulating possible trajectories.
- Reinforcement learning, training within the model itself instead of the physical world.
A practical nuance: the quality of predictions depends on the model's long-term consistency. Many systems accumulate errors over time, generating simulations that become unrealistic after several steps, which limits the depth of planning.