By decomposing credit assignment across both pipeline stages and within the planner's reasoning trace, DARS enables more efficient training of complex multi-stage image editing systems compared to naive joint optimization.
DARS is a reinforcement learning system for training instruction-based image editing that uses a two-stage pipeline (planner + renderer). It solves the credit assignment problem—figuring out whether failures come from bad planning or bad rendering—using structured reasoning outputs and multi-level reward analysis to provide targeted feedback to each component.