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ResOPD is established as an efficient, plug-and-play variance reduction primitive for sparse on-policy distillation, which provides unbiased full-vocabulary reverse KL gradient estimation under on-policy sampling, with substantial variance reduction under the same sparse payload in the evaluated settings.
This work presents WorldReward, a VLM-based pairwise preference reward model that unifies action-consistency and visual-quality evaluation for camera-conditioned world models, and introduces WorldReward-Bench, a human-annotated benchmark measuring reward-model agreement with human preferences across action consistency,...
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