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Current leading models fail to achieve over 60% success in constructing 3D worlds from user prompts, but VibeWorlder models leverage RL training to outperform them significantly.
REOPD's token-wise adaptability allows for more stable and reliable training, reducing the risk of reward hacking while enhancing performance across varied domains.
