Two months of π0.5 finetune ablations on a real manufacturing task: data quality beats volume
DominiqueCAPaul · x · 2026-09-24
DominiqueCAPaul published all results, data, and runs from two months of ablating π0.5 finetunes on a real manufacturing task, reaching a 98% success policy.
Key findings:
- Pure scaling was the weakest lever: 5x more data moved success from 63% to 76%.
- Diversity mattered more: 4h spread across five scenes beat 4h in the eval scene by 30pp.
- Quality was strongest: one clean, slower hour on top of 21h pushed 76% → 90%—more than the previous 17 hours combined.
- Quality alone worked too: 1h of clean data plus 240 human-intervention rollouts (1.7h total) went 28% → 88%.
Takeaway: in robot learning, data quality and diversity can matter far more than sheer volume.
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