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This paper introduces a hierarchical taxonomy and dense supervision training strategy for diffusion-based image editing, leveraging a large dataset of 12 million editing pairs and a granular evaluation benchmark to enhance model performance.
Introduces QVal, a training-free testbed for evaluating dense supervision signals in long-horizon LLM agent tasks by measuring alignment with Q-values, enabling fair comparison of different supervision approaches without training.