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The results support frozen verification as a training signal for evidence selection, while showing that strict boundary precision remains comparatively weaker.
The results support on-policy self-distillation as an efficient and analyzable approach to diffusion post-training by converting image-level reward guidance into explicit and continually refreshed intermediate supervision, thereby opening a path toward more efficient and diagnosable alignment.
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