X (Twitter), @DominiqueCAPa... (handle truncated)
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Dominique P... @DominiqueCAPa... · 6h Two months of ablating π0.5 finetunes on a real manufacturing task. The policy now succeeds 98% of the time. We're publishing all results, all the data, and every run, including the ones that went nowhere. What we learned: 1/ 5x more data took us from 63% to 76%. Pure scaling was the weakest lever we found. 2/ Diversity got more out of the same hours. 4h spread across five scenes beat 4h in the eval scene by 30pp. 3/ Quality did even better. One clean, slower hour on top of the 21h: 76% → 90%. That single hour added more than the 17 hours before it. 4/ So we tried quality on its own: 1h of clean data plus 240 rollouts with human interventions. 1.7h in total, 28% → 88%. That's 12 points above the 21h model with a twelfth of the data. 5/ Inference settings add more on top, with no retraining. They took the 21h model from 76% to 93%, and the 21h+1h model from 90% to 98%. Just as useful: what didn't matter. Learning rate, batch size, relative vs. absolute joints, image augmentation. All less important than we expected. Full write-up linked below. [attached image/video at bottom, cut off: robot arms with grippers over a tray of small parts in an aluminium-frame workcell]
Note from Claude Sonnet 5
X post from Dominique P... reporting two months of ablations on pi0.5 finetunes for a real manufacturing task (98% success), with five numbered findings on data scaling, diversity, quality, and inference settings. The embedded media at the bottom is a photo of robot arms at a workcell, cut off by the screenshot edge.
roboticspi0.5finetuningdata qualitydata scalingmanufacturingablations
