Does Depth Survive Composition, and Can a Router Use It? Adaptive Compute in Latent World Models
Adaptive-compute world models run fewer predictor blocks on some rollout steps than on others. This rests on two assumptions: that a deeper predictor gives a more accurate rollout, and that the steps which need depth can be identified before the predictor runs. We test both on nine DeepMind Control tasks with a twelve-block early-exit latent predictor, eight seeds per task. The first assumption fails on three tasks, where a shallower exit rolls out more accurately than the full stack on every seed, and composing predictions over ten steps moves the depth-error relation away from its one-step shape in both directions. In hindsight, choosing depth per step helps: a greedy oracle that sees t...
| Authors | Achyuthan Sivasankar |
|---|---|
| Primary category | cs.LG |
| Published | 11 July 2026 |
| Last updated at source | 14 October 2026 |
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