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.
ProofPaper ↗
Key points
- 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 Relative Policy Optimization (GRPO) that aligns update weighting with this structure.
- The rewriter jointly generates an enhanced prompt and its semantic partition.
- Ablation studies show that both semantic boundaries and pooling within spans contribute to these gains, supporting semantic structure as a practical inductive bias for prompt engineering with RL.
Sources (2)
- [1]SCOPE: Semantic Bounded Credit Optimization for Prompt Engineering in Text-to-image GenerationHugging Face Daily Papers · Oct 15, 12:00 AM
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.
- [2]SCOPE: Semantic Bounded Credit Optimization for Prompt Engineering in Text-to-image GenerationarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 15, 05:30 PM · same content
Extractive summary: sentences quoted from the sources.
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