October 9, 2025Open Access

Policy Learning from Large Vision-Language Model Feedback without Reward Modeling

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PLARE achieves competitive performance compared to existing VLM-based methods in robotic manipulation.
The approach eliminates the need for labor-intensive reward function design through preference label queries.
Experiments demonstrate effective application of PLARE in real-world robotic manipulation tasks.
This method improves safety by reducing reliance on costly online data collection.

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PLARE improves manipulation tasks in robotics using vision-language models, bypassing traditional reward modeling.

Luu et al. (2025) studied this question.

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