Reinforcement learning from human feedback

Enhancing image quality involves refining models through reinforcement learning from human feedback, utilizing cost-effective evaluation data from photo experts. Unlike the current method of manually creating supervised learning datasets for training enhancement models, which is both labor-intensive and costly, this process can be limiting in terms of data collection.

Reinforcement learning from human feedback (RLHF) offers a promising, more budget-friendly alternative to expanding dataset sizes. It enables the continuous enhancement of models without the need for continually amassing larger training datasets with diminishing returns