Fine-tuning π0.5 on a real factory task: data diversity beats raw scale, hitting 98% success
DominiqueCAPaul · x · 2026-09-24
Dream Machines' first blog post documents two months of ablation experiments fine-tuning Physical Intelligence's open-source VLA model π0.5 on a real manufacturing task from a German manufacturer, reaching 98% policy success rate. All results, data, and runs—including failures—are being published.
- Pure scaling was the weakest lever: 5x more data only improved success from 63% to 76%.
- Diversity got more from the same hours: 4 hours of data spread across five scenes beat 4 hours in a single scene.
The task requires two arms to transfer actuators from a box into a fixture with correct orientation. The authors note that neither the π0.5 paper nor the official repo offers fine-tuning recommendations, and no other company has published results, so these findings aim to save others time on tuning and data collection.
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