Image Reward Models

Train scoring models that evaluate image quality, aesthetics, and prompt alignment, enabling automated evaluation and reward signals for vision model fine-tuning. Image reward models are trained on human preference judgments (image A vs B given a prompt) to predict which image a human would prefer. They typically use a vision-language backbone (CLIP, BLIP) with a lightweight scoring head. The reward model outputs a scalar score for any image-prompt pair, serving as a proxy for human judgment. These scores are used for model evaluation, prompt engineering, and as reward signals in RL-based fine-tuning.