RENEW paper proposes method to repair model exploitation in world models using preferences.
Read the original at arxiv.org→arXiv:2607.14180v1 Announce Type: new Abstract: World models are widely used in offline reinforcement learning (RL) to improve sample efficiency and generate experience beyond a fixed dataset. However, they are...
Original headline: "RENEW: Towards Learning World Models and Repairing Model Exploitation from Preferences"