GM CT Training embodied AI models on raw video streams is notoriously inefficient because 90% of pixels in adjacent frames are redundant background noise. Feeding identical static walls over and over burns model compute without teaching the machine anything new about spatial physics. I track how @vangrid_io tackles this through keyframe entropy selection. Their ingestion stack filters for high-information delta frames, isolating dynamic state changes and critical viewpoint shifts before feeding the spatial pipeline. Efficiency in physical AI is built on tokenizing novelty, not hoarding repetitive pixels.