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EmoReward: Self-Evolving Reward Calibration for Emotional Video Generation RL

EmoReward is a new training mechanism that adaptively calibrates reward weights for emotional video generation in reinforcement learning. It uses reward traces from training runs to estimate reliability of different emotional evidence signals, then adjusts fusion weights in subsequent runs without updating reward models. Experiments on HunyuanVideo 1.5 show improvements across RL algorithms, with ablations on SD 3.5 and FLUX supporting transferability.

Why it matters: This introduces a concrete method to address reward hacking and subjective evaluation in emotional video generation, with experimental validation on multiple models.