Yurong You

[Marco Pavone @drmapavone]

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: https://t.co/Xg0LYaEd9H):

🚗 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): https://t.co/Xg0LYaEd9H
• @nvidia announcement: https://t.co/zWC2rbDK2W

#AutonomousVehicles #PhysicalAI #Robotics #AI #MachineLearning #ReinforcementLearning #OpenSource #NVIDIA #Alpamayo

@NVIDIADRIVE @NVIDIAAI

[Yurong You @YurongYou]

https://t.co/6t79PJUeuN Just released! Feel free to try it out!

[Marco Pavone @drmapavone]

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 (https://t.co/fD9eUcndya), 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:
https://t.co/P0nuqkwBab (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

[Marco Pavone @drmapavone]

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 (https://t.co/fD9eUcndya), 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:
https://t.co/P0nuqkwBab (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

[Marco Pavone @drmapavone]

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 (https://t.co/EmY9IRNXHZ).

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
🔗 https://t.co/ZJk5GGIbFV

Looking forward to seeing many of you there.

@NVIDIADRIVE @NVIDIAAI

[Maximilian Igl @MaxiIgl]

Simulation is essential for scaling the development of autonomous vehicles - from synthetic data generation to evaluation up to closed-loop training/reinforcement learning!

We're organizing the Third Workshop on "Simulation for Autonomous Driving" (SAD) at #CVPR2026 in Denver, CO, USA, to bring together leading experts advancing simulation fidelity and simulation-based training.

This half-day workshop features exciting keynotes from top researchers in the field, contributed papers, and a panel discussion - covering behavior modeling, sensor simulation, world models, reinforcement learning, and more.

Invited Speakers: @drmapavone (Stanford / NVIDIA), @AnguelovDrago (Waymo), @francislee2020 (University of Hong Kong), Siva Manivasagam (Waabi), @wucathy (MIT).

A big thank you to all speakers and organizers!

Organizers: @yiyi_liao_, @MaxiIgl , Kashyap Chitta, @AzadehDinparast, Maximilian Naumann, @ZGojcic, @stan188249301, Kate Tolstaya, @wangjksjtu, @FidlerSanja, @shimon8282.
📝 Call for Papers is opening soon! Submission deadline: March 12, 2026.

🔗 https://t.co/UgzpTG0dnR

[Marco Pavone @drmapavone]

🚀 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: https://t.co/H0ZxzXXsG6

-- Hugging Face Blog: https://t.co/EmY9IRNpSr

-- Tech Blog: https://t.co/htAOupt7Nz

-- Alpamayo 1 reasoning model: https://t.co/8PSdQNCSHg

-- Physical AI AV Dataset: https://t.co/fD9eUcmFIC

-- AlpaSim simulator: https://t.co/9WqutgoGfF

I’m incredibly proud of the @nvidia AV Research team (https://t.co/YI3eJrkbZQ) 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: https://t.co/D4Z0xLE8JX
[Sr.] Research Engineer: https://t.co/5yCpiDJ572

#AutonomousVehicles #AutonomousDriving #AI #Simulation #ReasoningAI #OpenEcosystem #Alpamayo @NVIDIAAI @NVIDIADRIVE

[Marco Pavone @drmapavone]

🚗 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 (https://t.co/zLPiF17QmW) 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: https://t.co/D4Z0xLEGzv
🔹 [Sr.] Research Engineer: https://t.co/5yCpiDJCWA

@NVIDIADRIVE @NVIDIAAI @nvidia

[Marco Pavone @drmapavone]

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: https://t.co/9nI9L08LJJ
Inference Code: https://t.co/QpPzLEsFnm
Paper: https://t.co/8PSdQNDqwO
Blog Post: https://t.co/S92N6ff58L

A subset of the data used to train and evaluate AR1 is available in the @nvidia Physical AI Open Datasets: https://t.co/fD9eUcndya

AR1 can be evaluated using AlpaSim (https://t.co/9Wqutgpe5d), @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.

[Yurong You @YurongYou]

So excited to see Alpamayo-R1 finally out! Working on this project felt like watching a baby grow up — and it’s still evolving fast :)

[Marco Pavone @drmapavone]

Excited to unveil @nvidia's latest work on #Reasoning Vision–Language–Action (#VLA) models — Alpamayo-R1!

Alpamayo-R1 is a new #reasoning VLA architecture featuring a diffusion-based action expert built on top of the #Cosmos-#Reason backbone. It represents one of the core technologies driving NVIDIA’s push toward Level 4 autonomy and robotaxis (https://t.co/IbGjWrBAfo), as announced by Jensen Huang at #gtc DC last week.

📄 Paper: Alpamayo-R1 https://t.co/8PSdQNDqwO

We present:
- Architecture & Design: How to transform a VLM into a driving-ready Reasoning VLA
- Chain of Causation Labeling: A new framework enabling reasoning-based learning
- Training Strategy: From internet-scale pre-training → AV-specific SFT → RL-based post-training
- Extensive Evaluation: From closed-loop simulation to real-world, on-vehicle testing

📈 Results: Alpamayo-R1 delivers significant performance gains over end-to-end baselines — especially in rare, safety-critical scenarios — all while maintaining real-time inference (99 ms end-to-end latency).

Coming soon: releases of model variants and reasoning metadata built on top of the Physical AI Dataset (https://t.co/fD9eUcndya)—with more updates on the way. Stay tuned!

🙌 Huge thanks to Wenjie Luo and @yan_wang_9 (project co-leads); the @nvidia AV Research team (@iamborisi, @YurongYou, @xinshuoweng, @tianran_, @wenhaoding95, and many others); collaborators across @nvidia Research (@liu_mingyu, @visualyang, @PavloMolchanov, and many others); and the @nvidia AV Product team (Sarah Tariq, Patrick Liu, Jack Huang, and many more). Full contributor list in the Appendix.

@NVIDIADRIVE @NVIDIAAI

[Marco Pavone @drmapavone]

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: https://t.co/wipS6tirF9

[Marco Pavone @drmapavone]

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: https://t.co/lYoLhRwrYm

[Boris Ivanovic @iamborisi]

Are you a PhD student excited to build the future of Autonomous Vehicles? The @nvidia Autonomous Vehicles Research Group is now recruiting PhD research interns for 2026!!

Apply here: https://t.co/bElo8saaBu

[Marco Pavone @drmapavone]

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: https://t.co/ZAzpxXxsDS — and be sure to select “Autonomous Vehicles.”

@NVIDIAAI

[Marco Pavone @drmapavone]

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: https://t.co/x870ZHVQYW

@NVIDIADRIVE

[Boris Ivanovic @iamborisi]

Happy to share our latest work on efficient sensor tokenization for end-to-end driving architectures! https://t.co/nkYUIzfyJT

We introduce a novel way to tokenize multi-camera input for AV Transformers that is resolution- and camera-count-agnostic, yet geometry-aware

🧵👇

[Yi-Ting Chen @chen_yiting_TW]

📢 The first X-Sense Workshop: Ego-Exo Sensing for Smart Mobility at #ICCV2025! 

🎤 We’re honored to host an outstanding speaker lineup, featuring Manmohan Chandraker, @BharathHarihar3, @wucathy, Holger Caesar, @zhoubolei, @Boyiliee, Katie Luo

https://t.co/FmVGnwv906

[Marco Pavone @drmapavone]

At #GTC2025, Jensen unveiled Halos, a comprehensive safety system for AVs and Physical AI. Halos integrates numerous technologies developed by my team @nvidia, and I was thrilled to help coordinate its launch alongside Riccardo Mariani and many amazing colleagues @NVIDIADRIVE.

[Boris Ivanovic @iamborisi]

Don’t miss this deep dive into the future of autonomous vehicles!

Excited to present about how foundation models are transforming AV technology with @ALVAREZ_JOSEM at #GTC25!

Check out all the session details below 👇

[Marco Pavone @drmapavone]

For the first time ever, @nvidia is hosting an AV Safety Day at GTC - a multi-session workshop on AV safety.

We will share our latest work on safe AV platforms, run-time monitoring, safety data flywheels, and more! #AutonomousVehicles #AI at #GTC25

➡️ https://t.co/KBIIHVLBDL

[Marco Pavone @drmapavone]

AI4I, the Italian Institute of Artificial Intelligence for Industry (https://t.co/kOtm499aYp), has launched an international call for Heads of R&D Units (https://t.co/MWH5tJr6wM).

This is a unique opportunity to shape the AI roadmap in Italy and beyond! @FabioPammolli