Predictive and Generative Objectives › Summary

The objective function is the world model's job description. Reconstruction and likelihood reward observation prediction and may spend capacity on task-irrelevant detail. Variational objectives add latent regularization whose strength and gradient routing affect posterior use. Contrastive and masked objectives instead emphasize information selected by their positives, masks, and targets; embedding targets can discard texture, whereas raw-pixel targets need not. Multi-step and overshooting losses train bounded recursive consistency at additional compute and optimization cost. Across multi-term objectives, gradient topology and shared-parameter norms matter alongside scalar weights.

Three principles tie the chapter together. First, evaluate the metrics and horizons used at deployment: low one-step error alone does not establish accurate long rollouts. Second, control sufficiency depends on the representation, objective, data, and downstream task, not on architecture alone. Third, there is no universal loss composition; instrument the training loop, monitor each term and its shared gradients, and change the objective in response to a specific measured failure.

A fourth principle is to track distinct quality axes without treating them as statistically independent. Predictive fidelity, representation quality, uncertainty calibration, and decision usefulness measure different properties, but can interact through the same model and data. None alone guarantees the others. A claim that an objective produces a useful world model therefore needs direct evidence on the intended decisions as well as predictive diagnostics.

The next chapter, Actions, Inverse Dynamics, and Latent Actions, takes the objective one level down: not just what the model predicts, but how actions enter the prediction. Does the model see the action as an input, infer it as a latent variable, or discover it through an inverse dynamics model? That question changes both the loss and the state, and it sets up the data-curation and pretraining questions that follow.