In our last blog post, we iterated towards P-JiT, a pixel-space architecture that cut attention context windows by 4× while converging in 11× fewer samples. Here, we train the P-JiT for 660M samples (just $21K in compute) as a stepping stone towards larger, video models. For us, this $21K training run is a proof-of-concept along the way to our next video model. We were surprised by how good ...