Fixed Priorities, Moving Rewards: What ReCAST Changes in Diffusion Training
TL;DR for operators An image-generation product may need to follow prompts, produce attractive images, and render specified text correctly. Its team can assign a fixed importance to each objective, but that does not establish when each objective’s feedback should influence training. ReCAST separates these decisions: it preserves specified reward budgets while reallocating their influence across diffusion denoising steps. In experiments on Stable Diffusion 3.5-Medium, this improves aggregate training rewards for text rendering but not for compositional generation; independent reward models nevertheless favor ReCAST on average in both settings. For training teams, the opportunity is more selective reward allocation without surrendering control of objective priorities. The tradeoff is expensive, approximate temporal profiling whose reliability beyond the tested configuration remains uncertain. ...