World Models in AI Systems

Training efficiency#

• Curriculum learning and self-play; prioritized sampling of difficult transitions
• Mixed-precision and gradient checkpointing for long sequences
• Periodic distillation to smaller models without losing planning fidelity

Real-time inference#

• Short-horizon planning with caches; amortize encoder costs
• Partial rollouts around candidate actions; reuse latents across samples
• Degrade gracefully: Switch to reactive policy when latency budget is exceeded

MLOps for world models#

• Dataset versioning tied to environment builds and seeds
• Rollout reproducibility and regression suites for planning tasks
• Safety gates for deployment: OOD monitors, conservative fallback modes, human-in-the-loop controls