Renew: Towards learning world models and repairing model exploitation from 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"
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- Jul 17, 04:00 UTC arXiv cs.LG lead source RENEW: Towards Learning World Models and Repairing Model Exploitation from Preferences