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: arxiv.org/pdf/2610.08761 🌐 Website: veri-fine.github.io/ 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

We are hiring! The Autonomous Systems and Physical AI Research (ASPIRE: research.nvidia.com/labs/asp…) group at @nvidia is looking for talented PhD Research Interns to join us in advancing the frontiers of #autonomous #systems and #Physical #AI. We work across a broad range of research areas, including #reasoning models, generative simulation, #agentic AI workflows, and Physical AI #safety, with applications spanning autonomous vehicles and a broad range of Physical AI systems. Interested in pushing the limits of what’s possible? Apply now: nvidia.wd5.myworkdayjobs.com…

We have just released #Alpamayo 2 Super — @nvidia’s frontier open reasoning model for #autonomous #vehicles. NVIDIA Alpamayo 2 Super is an open 34-billion-parameter #reasoning vision-language-action (#VLA) model designed to accelerate autonomous vehicle (AV) development. It combines the 32-billion-parameter NVIDIA #Cosmos 3 Super Reasoner with a 2-billion-parameter diffusion-based Action Expert and is post-trained with #reinforcement #learning. Two aspects make Alpamayo 2 Super particularly exciting: 1. Open and commercially deployable: Alpamayo 2 Super is available on Hugging Face under OpenMDW-1.1, the Linux Foundation’s permissive license for open AI model distribution. The OpenMDW license is now being applied across the entire Alpamayo model family, enabling developers to deploy these models commercially without requiring additional permissions. 2. A multi-task foundation model for autonomous driving: Alpamayo 2 Super produces five tightly coupled outputs: - A trajectory describing the vehicle’s planned path. - A chain-of-causation (CoC) trace explaining the reasoning behind each driving decision, achieving benchmark-leading reasoning performance at frontier scale. - A meta-action (e.g., yield, lane change, stop) capturing the model’s intent. - Reasoning auto-labels that generate CoC annotations for training and validation data. - Visual question answering responses with 2D visual grounding, linking answers to specific regions in camera images. These multi-task capabilities enable developers to leverage a single foundation model across more of the development process, simplifying tooling and accelerating iteration. Resources: 🔹 Interview: youtube.com/watch?v=VeYZ-MrJ… 🔹 Video: youtube.com/watch?v=xySgVPLY… 🔹 Blog: blogs.nvidia.com/blog/alpama… 🔹 Technical blog: developer.nvidia.com/blog/ge… 🔹 Hugging Face blog: huggingface.co/blog/nvidia/n… 🔹 Model weights: huggingface.co/nvidia/Alpama… 🔹 Inference code: github.com/NVlabs/alpamayo2 As @JensenHuang has emphasized, open models help advance safety and security. We hope Alpamayo 2 Super will contribute to this vision by enabling researchers and developers around the world to build, experiment, and innovate in autonomous driving. We are excited to see what the community builds with Alpamayo 2 Super. @NVIDIADRIVE @NVIDIAAI

Excited to share the latest expansion of the @nvidia #Alpamayo open platform for reasoning-based autonomous vehicles. Since its launch earlier this year, Alpamayo has seen rapid adoption across industry and academia, with its reasoning models surpassing 400,000 downloads and earning a #COMPUTEX 2026 Best Choice Award. As announced by Jensen Huang during his #COMPUTEX keynote, we are now introducing several major additions designed to accelerate the development of next-generation AV systems (more details here: huggingface.co/blog/drmapavo…): 🚗 Alpamayo 2 Super — a new 32B-parameter driving foundation model with: • Full 360° surround-view perception • Advanced reasoning capabilities and chain-of-causation outputs • Meta-actions such as lane changes, yielding, and stopping • Reasoning auto-labeling and visual grounding for scalable data annotation • State-of-the-art performance across reasoning, prediction, and alignment tasks 🔄 AlpaGym — an open-source framework for closed-loop reinforcement learning, enabling AV models to learn from the consequences of their actions in simulation and helping bridge the gap between training and real-world deployment. 📊 New Open Benchmarks — including challenges for closed-loop driving and long-tail reasoning to help the community measure progress and drive innovation. 🛠️ Alpamayo Recipes — a centralized repository of end-to-end workflows covering supervised fine-tuning, reinforcement learning, quantization, and model customization. Reasoning models and closed-loop training are becoming foundational technologies for autonomous systems. Our goal is to provide the open tools, models, infrastructure, and benchmarks needed to accelerate progress across the entire AV ecosystem. A huge thank you to the many researchers, engineers, and community members whose feedback helped shape this release. Resources: • Overview of the latest Alpamayo release (note: some components will be released over the coming weeks): huggingface.co/blog/drmapavo… • @nvidia announcement: nvidianews.nvidia.com/news/n… #AutonomousVehicles #PhysicalAI #Robotics #AI #MachineLearning #ReinforcementLearning #OpenSource #NVIDIA #Alpamayo @NVIDIADRIVE @NVIDIAAI

Jensen today announced Alpamayo 1.5 at #NVIDIAGTC! #Alpamayo 1.5 is a major update to Alpamayo 1—@nvidia’s open 10B-parameter chain-of-thought reasoning VLA model, first introduced at #CES. Built on the #Cosmos-Reason2 VLM backbone and post-trained with RL, it adds support for navigation guidance, flexible multi-camera setups, configurable camera parameters, and user question answering. The result is an interactive, steerable reasoning engine for the AV community. We’re also releasing post-training scripts to help researchers and developers adapt the model. Additionally, we’ve significantly expanded the Alpamayo open platform across data and simulation, including releasing highly requested reasoning labels for the PhysicalAI Autonomous Vehicles dataset (huggingface.co/datasets/nvid…), as well as our chain-of-causation auto-labeling pipeline. 🔎 Learn more about Alpamayo 1.5 and the latest extensions to the Alpamayo open platform: huggingface.co/blog/drmapavo… (please note that most of the links will become active in the next few days.) Happy building—and stay tuned for more in the coming months! @NVIDIADRIVE @NVIDIAAI

What does it take to build autonomous vehicles that can reason about the world they drive in? Tomorrow at #NVIDIAGTC, Patrick Liu and I will take a deep dive into the #Alpamayo #reasoning model family—a family of reasoning-based vision–language–action (#VLA) models that form a core component of the Alpamayo open platform (huggingface.co/blog/drmapavo…). We’ll cover three main topics: - How reasoning-based VLA models like Alpamayo 1 are designed and built - What it takes to bring Alpamayo 1 to production, including some of our latest results - Several exciting announcements about the expansion of the Alpamayo open platform If you're working on autonomous driving, robotics, or foundation models for physical AI, this session will offer a look at where the field is heading. Session details: 📅 Monday, Mar 16 | 3:00 PM PDT 📍 #NVIDIAGTC 2026 🔗 nvda.ws/4rze5oj Looking forward to seeing many of you there. @NVIDIADRIVE @NVIDIAAI

🚀 Exciting news from #CES2026! In his keynote today, Jensen announced @nvidia Alpamayo — a *fully open* ecosystem of models, simulation tools, and datasets designed to accelerate reasoning-based autonomous vehicle (AV) architectures and advance the path to Level 4 autonomous driving. Alpamayo brings together several technologies we’ve developed to enable reasoning-based vision–language–action (VLA) models for AVs. Our goal is to provide researchers and developers with a flexible, fast, and scalable platform for evaluating and training reasoning-based AV architectures in realistic closed-loop settings. Explore Alpamayo: -- Press Release: nvidianews.nvidia.com/news/a… -- Hugging Face Blog: huggingface.co/blog/drmapavo… -- Tech Blog: developer.nvidia.com/blog/bu… -- Alpamayo 1 reasoning model: research.nvidia.com/publicat… -- Physical AI AV Dataset: huggingface.co/datasets/nvid… -- AlpaSim simulator: github.com/NVlabs/alpasim I’m incredibly proud of the @nvidia AV Research team (research.nvidia.com/labs/avg…) and our many @nvidia collaborators whose contributions made this possible. More releases and features are coming soon — we can’t wait to see what the community builds with Alpamayo! 💡 Want to help grow the Alpamayo ecosystem? We’re hiring: [Sr.] Research Scientist: nvidia.wd5.myworkdayjobs.com… [Sr.] Research Engineer: nvidia.wd5.myworkdayjobs.com… #AutonomousVehicles #AutonomousDriving #AI #Simulation #ReasoningAI #OpenEcosystem #Alpamayo @NVIDIAAI @NVIDIADRIVE

🚗 Imitation learning is everywhere—but is it enough? So far, imitation learning—most commonly via behavior cloning (BC)—remains the go-to approach for training real-world autonomous vehicle (AV) driving policies. Yet BC operates in an open-loop (OL) fashion, overlooking the critical interdependence among inputs, outputs, and future states that comes with closed-loop (CL) operation. The result? The notorious—but often overlooked—OL–CL gap ⚠️ To address this challenge and encourage broader adoption of CL techniques, we’ve just published a survey (research.nvidia.com/publicat…) presenting a comprehensive taxonomy of closed-loop training methods for end-to-end driving. Our framework organizes approaches along three key axes: - Action generation - Environment response generation - Training objectives 💡 Bottom line: enabling technologies—like neural rendering, generative world models, and scalable RL—have now matured, making closed-loop AV training ready for wide-scale adoption. We’d love to hear your thoughts—drop a comment and join the discussion! 💬 And as a reminder, we are hiring for full-time research scientist and research engineer positions: 🔹 [Sr.] Research Scientist: nvidia.wd5.myworkdayjobs.com… 🔹 [Sr.] Research Engineer: nvidia.wd5.myworkdayjobs.com… @NVIDIADRIVE @NVIDIAAI @nvidia

We’ve just released @nvidia #DRIVE Alpamayo-R1 (AR1) — the world’s first industry-scale open #reasoning #VLA model for autonomous-vehicle (AV) research. AR1 integrates Chain-of-Causation reasoning with trajectory planning to improve decision-making in complex driving scenarios. Built on @nvidia #Cosmos #Reason, AR1 is designed as a customizable foundation for a broad range of AV applications — from instantiating an end-to-end backbone for autonomous driving to powering advanced, reasoning-based auto-labeling tools. Resources: Model: huggingface.co/nvidia/Alpama… Inference Code: github.com/NVlabs/alpamayo Paper: research.nvidia.com/publicat… Blog Post: blogs.nvidia.com/blog/neurip… A subset of the data used to train and evaluate AR1 is available in the @nvidia Physical AI Open Datasets: huggingface.co/datasets/nvid… AR1 can be evaluated using AlpaSim (github.com/NVlabs/alpasim), @nvidia's newly released open-source AV simulation framework built specifically for research and development. (Separate post on AlpaSim coming soon.) This release completes @nvidia’s trifecta — model, data, and simulator — to accelerate research and development in the autonomous-vehicle domain. Happy developing, and stay tuned for more! Huge thanks to the phenomenal team that made this possible @NVIDIAAI @nvidia.

Simulation is one of the fastest-growing technologies in Physical AI. It’s now widely used for both testing and training—but can it also be applied to safety validation, where accurate estimates of safety metrics are critical? Join me and my @NVIDIAAI colleagues, @apoorva__sharma and Rachel Luo, for a live session where we will be discussing how to accelerate AV safety validation through simulation. 📅 Wednesday, Oct 22 | 9–10 AM PDT 🔔 Add to calendar: nvda.ws/3KHf99n

The Autonomous Vehicle (AV) Research Group @NVIDIAAI is looking for talented interns! Dive into cutting-edge work—from reasoning models and generative simulation to AI safety—and help shape the future of AV and embodied AI. Ready to push the limits? Apply now: nvidia.wd5.myworkdayjobs.com…

We’re now accepting applications for the 2026–2027 NVIDIA Graduate Fellowships! If you’re passionate about advancing cutting-edge reasoning models for Physical AI applications 🚗🤖, apply here: research.nvidia.com/graduate… — and be sure to select “Autonomous Vehicles.” @NVIDIAAI

Can we use simulation to validate Physical AI? Yes—with far fewer real-world tests. We propose a control variates–based estimation framework that pairs sim & real data to dramatically cut validation costs. #AI #Robotics #Sim2Real" Paper: arxiv.org/pdf/2506.20553 @NVIDIADRIVE