A paper titled "EmoReward: Self-Evolving Reward Calibration for Emotional Video Generation RL" proposes a self-evolving reward calibration method for reinforcement learning post-training of emotional video generation, addressing the lack of a uniquely correct target in subjective aesthetic and emotional evaluation. The work builds on Group Relative Policy Optimization (GRPO), which the authors note has advanced generative model post-training in verifiable domains such as code and mathematics but does not transfer cleanly to subjective video quality. The paper targets the gap left by existing approaches that the authors say typically fall short on emotional expression evaluation.
Group Relative Policy Optimization (GRPO) has advanced generative model post-training in verifiable domains such as code and mathematics, but evaluating video quality, especially emotional expression, often depends on subjective aesthetics and has no uniquely correct target. Existing approaches typi
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