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The results suggest that dense to MoE adaptation with dynamic expert deactivation is a practical direction for reducing active VLA model size without severe performance loss.
This work proposes to adopt fatigue as a proxy of the finite energy limit, inject it into general character animation, and thoroughly investigate how fatigue introduces new characteristics to physics-based character control.
A system level acceleration strategy that reduces computation in both perception and action generation and compress diffusion sampling into a compact 2-step schedule through efficiency oriented training while preserving action precision is proposed.
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