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About this paper
Yixiao Wang, Peiyi Chen, Mustafa Mukadam, Shreekant Gayaka, Masayoshi Tomizuka, Son Tran
Latent RL Prior transfers a simulated reinforcement-learning controller to visual manipulation by distilling its action behavior into a frozen decoder and training an image pathway to supply task context from demonstrated actions alone. In simulated door lifting it beat full-parameter-fine-tuned VLA baselines using 50 demonstrations versus 500, reducing downstream visual demonstrations; its prior required 100,000 successful simulated RL rollouts.
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