Symbolic supervision gives video models equal reasoning at ~1/30 the compute, Rubik's Cube study finds

niloofar_mire · x · 2026-10-03

A new arXiv paper tests hidden-state reasoning in video generation models with a 2x2x2 Rubik's Cube benchmark: given a fixed view of three faces, models must predict the sticker configuration after nine prescribed moves.
Key finding 1: validation MSE follows an approximate power law, but lower loss does not reliably indicate downstream reasoning. A 70M autoregressive model hits 44.6% frame accuracy at 0.1 PF-days, while a 1B model sits at 0.3% and only reaches 83.7% at 3.14 PF-days.
Key finding 2: adding symbolic state supervision lifts a 20M model's frame accuracy from 31.1% to 67.3% at the same data budget — matching video-only scaling with roughly 1/30 the compute. Learning scalable, generalizable symbolic representations may be a crucial complement to pure scaling.

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