ReWorld: Multi-Dimensional Reward Modeling for Embodied Video Generation
Overview
This research presents ReWorld, a framework that applies reinforcement learning to improve physical fidelity, dynamic consistency, and task logic in embodied video generation. The authors construct a large-scale (~235K) video preference dataset to train a hierarchical reward model capturing multi-dimensional human-aligned rewards. A practical alignment algorithm post-trains flow-based video generators using this reward via an efficient PPO-style method. Experiments show ReWorld significantly enhances physical fidelity, logical coherence, embodiment plausibility, and visual quality compared to prior methods.
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Reported quantity · video preference dataset size: 235000 other · Basis not reported
“o generation with physical realism, task completion capability, embodiment plausibility and visual quality. Specifically, we first construct a large-scale (~235K) video preference dataset and employ it to train a hierarchical reward model designed to capture multi-dimensional reward consistent with human preferences. We further propose a practical alignment algorithm that post-trains flow-based video generators using”
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Dataset · modalities: video
“o generation with physical realism, task completion capability, embodiment plausibility and visual quality. Specifically, we first construct a large-scale (~235K) video preference dataset and employ it to train a hierarchical reward model designed to capture multi-dimensional reward consistent with human preferences. We further propose a practical alignment algorithm that post-trains flow-based video generators using”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalReWorld: Multi-Dimensional Reward Modeling for Embodied Video Generation
This research presents ReWorld, a framework that applies reinforcement learning to improve physical fidelity, dynamic consistency, and task logic in embodied video generation. The authors construct a large-scale (~235K) video preference dataset to train a hierarchical reward model capturing multi-dimensional human-aligned rewards. A practical alignment algorithm post-trains flow-based video generators using this reward via an efficient PPO-style method. Experiments show ReWorld significantly enhances physical fidelity, logical coherence, embodiment plausibility, and visual quality compared to prior methods.
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