NEW RESEARCH: You can now play Jenga with your robot! Called SPD, for Pre-training Visual Dexterity in Simulation, it is a research method from Stanford, MIT and Scale AI. It involves @aseemrd, @chelseabfinn, and others. They define a training method for dexterous bimanual manipulation. It starts by pre-training a 222M-parameter diffusion-transformer policy on data collected by five people teleoperating a virtual 56-DoF two-arm, two-dexterous-hand robot in VR (75 hours over one week, across six scenes and hundreds of objects), then fine-tunes that policy on one to two hours of real teleoperated demonstrations per task. The deployed policy is autonomous, runs at 30 Hz with 8-step action chunks, and uses multi-view cameras plus proprioception. It is demoed on five tasks: plate racking, cup stacking, Jenga, mug hanging and bottle tossing. While Dyna-2 scales passive human video (a million hours), and the LeCun/Pinto camp collects cheap real robot demos, SPD collects teleoperated simulation data. Five people driving a virtual 56-DoF bimanual robot in VR for 75 hours, then one to two hours of real demos per task to bridge. For high-DoF dexterity, real teleoperation is expensive and fragile (a breakable physical rig, constant resets, one setup at a time), while teleoperating a sim is cheap, safe, parallel and resettable, so sim teleop is the only way to amass dexterous demonstration coverage that hardware cannot practically produce. Two dexterous multi-fingered hands plus arms is 56 degrees of freedom, a space so large that collecting enough real demonstrations to cover it is prohibitive. VR teleop in sim let five operators span six scenes and hundreds of objects in a single week, coverage no one real 56-DoF rig could survive collecting, so the method is aimed squarely at the dexterity ceiling where data scarcity bites hardest. What I like about it, is that the results are presented as relative task progress and training-loss curves, not absolute success rates. Sim pre-training beats behavior-cloning-from-scratch on all five tasks and that lower pre-training loss tracks real performance, but publishes no per-task success percentage. So this is about the trend (pretraining helps, loss predicts success), not a deployable reliability number, the same trend-not-capability framing as Dyna-2 and the recurring reliability wall in this file. -> Pre-training loss predicts real-world performance. That correlation is exactly what justifies scaling sim teleop data (iterate cheaply in sim, expect real gains). A 222M model fine-tuned on one to two hours of real data is the prior-beats-data thesis again, with the prior built from sim teleop.