Yurong You

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/nvidi…), 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/drmapavon… (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

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 (nvidianews.nvidia.com/news/nvidia-ub…), as announced by Jensen Huang at #gtc DC last week. 📄 Paper: Alpamayo-R1 research.nvidia.com/publication/20… 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 (huggingface.co/datasets/nvidi…)—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

💡 Learn about leveraging foundation models such as vision-language models and video generation models for #autonomousvehicle development in this new #GTC25 session on Mar. 19, 4pm: Research to Production: Transforming AV Technology With AI [S72707] ➡️ nvda.ws/4kDXr49