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Finn presents Physical Intelligence's own results, and the headline is significant: their π₀7 foundation model now matches or beats specialist fine-tuned models across tasks out of the box, the same transition language models went through around GPT-2/GPT-3. She also details the concrete engineering that got them there — an RL recipe with human interventions, multi-timescale memory, and metadata prompting that lets low-quality data help rather than hurt.

• π₀7 out-of-the-box matches or exceeds RL post-trained specialist models across multiple tasks — the strongest evidence yet of generalist robot models.
• Their espresso robot hit 90%+ success and ran autonomously for 13 hours, the reliability bar Finn argues real-world robotics requires.
• Adding low-quality training data improved performance with metadata prompting but degraded it without — how you label data matters as much as what you add.
• The models are already deployed commercially (laundry folding at Ultra, warehouse packaging at Weave) and adapted to drones, surgical robots, and tractors.

Parte de Edición Nº 005: How Unify cut agent costs 95%, and why RL-trained agents break in the wild