SCOPE: Semantic Bounded Credit Optimization for Prompt Engineering in Text-to-image Generation
SCOPE improves text-to-image generation by using reinforcement learning to rewrite prompts. It addresses a problem where credit is fragmented across subword tokens. SCOPE introduces an objective that aligns update weighting with coherent semantic spans within the prompt, pooling policy shifts to derive shared coefficients for token losses and retaining a single advantage per completion.