DARS: Dual-Level Credit Assignment RL with Structured Reasoning for Instruction-Based Image Editing
Instruction-based image editing uses a planner-renderer pipeline: a vision-language model (VLM) first converts the instruction into an edit plan, and a diffusion model then executes that plan. Training such systems with only final-image rewards is inefficient because a poor edit does not reveal whether additional optimization should place more emphasis on the planner or the renderer, and even planner-dominant cases remain difficult to localize within a free-form reasoning trace. We present DARS, a reinforcement learning framework for dual-level credit assignment in this two-stage setting. Across modules, multi-plan multi-render rollouts estimate between-plan and within-plan reward variabi...
| Authors | Haoxiang Cao, Jiajiong Cao, Xuanpu Zhang et al. |
|---|---|
| Primary category | cs.AI |
| Published | 20 August 2026 |
| Last updated at source | 20 August 2026 |
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