g images with
hundreds of predefined pipelines, and poor generalization beyond memorized
training examples. We introduce a novel reinforcement learning-based framework
that addresses these inefficiencies. Our approach first trains an ensemble of
reward models capable of predicting image quality scores directly from
prompt-workflow combinations, eliminating the need for costly image generation
during training. We then implement a two-phase training strategy: initial
workflow vocabulary training followed by G