Generating Critic-Guided Synthetic Experience in Latent World Models
arXiv:2610.39100v1 Announce Type: cross Abstract: Learning from offline datasets is limited in scalability, as collecting new experience is often costly or unsafe in the real world. World models offer an alternative way to generate new experience through a learned dynamics model, yet learning policies in latent space is limited due to compounding errors in imagined rollouts. To this end, we propose using the world model as a local transition generator, leveraging critic guidance to choose actio…