An abstract introduces ZAP, a robot agent trained on simulated tasks that its authors say succeeds on real-world tasks without real-robot training data.
Why it matters: For agent builders, the reported results suggest a way to separate pretrained task understanding from simulation-trained action skills, while avoiding real-robot data in training. The Franka result also indicates that transfer across embodiments remains a distinct, measured test rather than a claim of uniform success.
An arXiv abstract reports a method for selecting preference ties that reduces spurious reliance using fewer ties than uniform selection.
Why it matters: For researchers and builders using preference optimization, the work suggests tie selection may reduce spurious reliance without adding as many ties as uniform augmentation. The abstract also highlights that direct influence estimates can depend on information or curvature estimates that are unavailable or expensive to obtain.