A World Model Trains Robots, Then Steps Aside

What Changed

The world model is employed as a training component rather than a permanent deployment module.

Why It Matters

This approach could reduce inference-time compute, memory use, and system complexity for embodied AI. It also suggests that world models may be valuable as training scaffolds even when they are not suitable for direct control at runtime.

What To Do Next

Prototype a train-then-remove pipeline in MuJoCo, comparing a policy trained with a world-model auxiliary loss against an identical policy trained without it.