Reward as An Agent for Embodied World Models.

arXiv:2606.19990v3 Announce Type: replace Abstract: While RL has become a promising tool for refining world models, existing methods largely rely on conservative rollouts near the training distribution, limiting exploration, behavioral diversity, and richer dy