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This work recast the problem as selective trust and introduces MIST, a human-annotated benchmark that renders each reasoning item under four matched conditions, together with SC2W, a paired metric counting how often a misleading signal flips a clean-correct answer to wrong.
This work organizes the embodied data ecosystem as a pyramidspanning five complementary sources: real-robot data, UMI-style data, egocentric and exocentric data, simulation data, and general vision-language data, and further characterize each source in terms of data quality, diversity, reusability, and physical fidelit...
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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