CAST: Causal Advantage-Structured Training with Spatially Grounded Compositional Rewards for Diffusion Models
CAST is a new reinforcement learning method for fine-tuning diffusion models to improve image generation quality. It addresses limitations like reward saturation and sample inefficiency by identifying optimal denoising steps based on scene layout formation. CAST rewards distinct semantic units independently using pixel-space grounding, leading to significant performance gains.