Does Depth Survive Composition, and Can a Router Use It? Adaptive Compute in Latent World Models
Read the original on arXiv Machine Learning →The paper investigates whether deeper predictor blocks in adaptive‑compute world models truly improve rollout accuracy and whether a router can select the appropriate depth per step. Experiments on nine DeepMind Control tasks show that deeper predictors do not always yield better accuracy, and that a greedy oracle selecting depth per step can reduce error by up to 37% compared to fixed‑depth mixtures, yet no trained router achieves this advantage. The study also finds that supervising early exits only at the first rollout step can mitigate depth‑inversion issues observed in some tasks.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.