SCOPE: Semantic Bounded Credit Optimization for Prompt Engineering in Text-to-image Generation
Reinforcement learning (RL) enables language models to improve text-to-image (T2I) generation by rewriting prompts for a frozen image generator. Existing methods typically apply a single signal derived from the image to individual subword tokens, although objects, counts, attributes, and relations form coherent semantic spans. We propose SCOPE, an objective based on Group Relat
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Reinforcement learning (RL) enables language models to improve text-to-image (T2I) generation by rewriting prompts for a frozen image generator. Existing methods typically apply a single signal derived from the image to individual subword tokens, although objects, counts, attributes, and relations form coherent semantic spans. We propose SCOPE, an objective based on Group Relat
This research introduces a novel RL-based prompt engineering technique that improves text-to-image generation efficiency by aligning updates with semantic structures. It offers a method to enhance model performance without requiring additional image renders or separate span rewards, which is crucial for cost-effective development.