Zetta ζ Tops HuggingFace Daily Papers at 152 Upvotes: Robots That Update Skills Mid-Task, 90.8% on LIBERO-Pro and 11.1x Faster Inference
Zetta ζ (arXiv 2608.16590, submitted August 17) is a closed-loop embodied harness that lets robotic agents learn during task execution rather than only between episodes, using three timescale-separated loops (action-frequency governance, rollout-level critic-recovery proposal, and validation-gated skill updates) over a frozen base policy. Reported results are 90.8% success on LIBERO-Pro, 93.6% on RoboCasa, and an 11.1x inference speedup, with zero-shot transfer of learned skills and performance that keeps climbing with more self-exploration. It is today's runaway top paper on HuggingFace at 152 upvotes against 30 for the runner-up, and the validation-gated skill update pattern maps cleanly onto any self-improving agent loop, not just robotics.
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