What if AI could improve not only its own policies, but also the judges that evaluate them? As AI policies improve, they encounter new challenges and develop new failure modes. The judges evaluating them need to evolve as well. Otherwise, they can become a bottleneck to further progress. This is particularly relevant for #Physical# #AI#, where good decisions require spatial grounding, causal reasoning, and safety awareness. In our new paper, we introduce VeriFine, an #agentic# #harness# for continuous self-improvement in Physical AI, where policies, training curricula, and judges evolve together. The idea is to build agentic systems that can identify policy failures, turn them into useful training signals, and improve the policy. When the judge itself becomes a bottleneck, the harness brings in targeted human feedback to refine its evaluations and guide the next round of improvement. We evaluate VeriFine on autonomous driving and robot navigation, demonstrating continued improvements in both policy and judge capabilities through reinforcement learning and supervised fine-tuning. Key takeaway: We're developing agentic harnesses that go beyond optimizing a policy in a fixed loop. They adapt the improvement process itself—including how failures are identified, how training data is selected, and how progress is evaluated. 📄 Paper: 🌐 Website: Work led by Zewei Zhou, in collaboration with Rachel Luo Yulong Cao @ChaoweiX Chensheng Peng @Boyiliee @tianran_ Zheng Lian @yan_wang_9 Jiaqi Ma @iamborisi @wenhaoding95 #PhysicalAI# #Robotics# #AgenticAI# #Alpamayo# @NVIDIADRIVE @NVIDIAAI