Unified Reward Model for Multimodal Understanding and Generation

Understand

Recent advances in human preference alignment have significantly improved multimodal generation and understanding.

• A key approach is to train reward models that provide supervision signals for preference optimization.
• However, existing reward models are often task-specific, limiting their adaptability across diverse visual applications.
• We also argue that a reward model that jointly learning to assess multiple vision tasks may foster a synergistic effect, where improved image understanding enhances image generation assessment, and refined image evaluation benefits video assessment through better frame analysis.