WAM-OPD: On-Policy Distillation for World Action Models
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来源：arXiv cs.RO发布时间待核实
arXiv:2608.22364v1 Announce Type: cross Abstract: World action models (WAMs) couple visual future prediction with robot action generation, but accelerated students can lose task capabilities during distillation and later encounter states that are poorly represented by offline data. We study whether on-policy distillation (OPD) can repair such a student without requiring sparse-reward reinforcement learning.