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LLM-generated rewards in RL can be misleading early in training, but RHyVE dynamically selects the best reward signal based on policy competence, leading to improved performance.
Achieve state-of-the-art panoramic segmentation by training on local perspective views and generalizing to full 360掳 images, even with geometric distortions and unseen classes.
Existing affordance prediction models fall flat when confronted with the wide-angle, distorted reality of panoramic vision, but a new training-free pipeline called PAP rises to the challenge.
