World Models in AI Systems

Data and curriculum#

• Start with simple, predictable regimes; gradually increase complexity and noise
• Balance random exploration with task-directed data to avoid narrow models
• Maintain a replay buffer with diverse trajectories and hard negatives

Objectives and signals#

• Representation: Reconstruction, contrastive, predictive coding, or hybrid objectives
• Dynamics: Next-latent prediction; reward and termination modeling; consistency across rollout lengths
• Regularization: KL/entropy terms, spectral or weight decay, dropout, information bottlenecks

Stabilization#

• Short-horizon rollout training before extending horizons
• Scheduled sampling and partial teacher forcing
• Early stopping on long-horizon prediction error and planning success

Validation checkpoints#

• Hold-out environments or seeds for predictive accuracy and planning success rate
• Uncertainty calibration measures and out-of-distribution (OOD) detection
• Ablations for each module (V/M/C) to isolate failure modes