Alpamayo-R1: Bridging Reasoning and Action Prediction for Generalizable Autonomous Driving in the Long Tail
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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866918274749431808 |
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| author | NVIDIA : Wang, Yan Luo, Wenjie Bai, Junjie Cao, Yulong Che, Tong Chen, Ke Chen, Yuxiao Diamond, Jenna Ding, Yifan Ding, Wenhao Feng, Liang Heinrich, Greg Huang, Jack Karkus, Peter Li, Boyi Li, Pinyi Lin, Tsung-Yi Liu, Dongran Liu, Ming-Yu Liu, Langechuan Liu, Zhijian Lu, Jason Mao, Yunxiang Molchanov, Pavlo Pavao, Lindsey Peng, Zhenghao Ranzinger, Mike Schmerling, Ed Shen, Shida Shi, Yunfei Tariq, Sarah Tian, Ran Wekel, Tilman Weng, Xinshuo Xiao, Tianjun Yang, Eric Yang, Xiaodong You, Yurong Zeng, Xiaohui Zhang, Wenyuan Ivanovic, Boris Pavone, Marco |
| author_facet | NVIDIA : Wang, Yan Luo, Wenjie Bai, Junjie Cao, Yulong Che, Tong Chen, Ke Chen, Yuxiao Diamond, Jenna Ding, Yifan Ding, Wenhao Feng, Liang Heinrich, Greg Huang, Jack Karkus, Peter Li, Boyi Li, Pinyi Lin, Tsung-Yi Liu, Dongran Liu, Ming-Yu Liu, Langechuan Liu, Zhijian Lu, Jason Mao, Yunxiang Molchanov, Pavlo Pavao, Lindsey Peng, Zhenghao Ranzinger, Mike Schmerling, Ed Shen, Shida Shi, Yunfei Tariq, Sarah Tian, Ran Wekel, Tilman Weng, Xinshuo Xiao, Tianjun Yang, Eric Yang, Xiaodong You, Yurong Zeng, Xiaohui Zhang, Wenyuan Ivanovic, Boris Pavone, Marco |
| contents | End-to-end architectures trained via imitation learning have advanced autonomous driving by scaling model size and data, yet performance remains brittle in safety-critical long-tail scenarios where supervision is sparse and causal understanding is limited. We introduce Alpamayo-R1 (AR1), a vision-language-action model (VLA) that integrates Chain of Causation reasoning with trajectory planning for complex driving scenarios. Our approach features three key innovations: (1) the Chain of Causation (CoC) dataset, built through a hybrid auto-labeling and human-in-the-loop pipeline producing decision-grounded, causally linked reasoning traces aligned with driving behaviors; (2) a modular VLA architecture combining Cosmos-Reason, a vision-language model pre-trained for Physical AI, with a diffusion-based trajectory decoder that generates dynamically feasible trajectories in real time; (3) a multi-stage training strategy using supervised fine-tuning to elicit reasoning and reinforcement learning (RL) to enforce reasoning-action consistency and optimize reasoning quality. AR1 achieves up to a 12% improvement in planning accuracy on challenging cases compared to a trajectory-only baseline, with a 35% reduction in close encounter rate in closed-loop simulation. RL post-training improves reasoning quality by 45% and reasoning-action consistency by 37%. Model scaling from 0.5B to 7B parameters shows consistent improvements. On-vehicle road tests confirm real-time performance (99 ms latency) and successful urban deployment. By bridging interpretable reasoning with precise control, AR1 demonstrates a practical path towards Level 4 autonomous driving. Model weights are available at https://huggingface.co/nvidia/Alpamayo-R1-10B with inference code at https://github.com/NVlabs/alpamayo. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_00088 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Alpamayo-R1: Bridging Reasoning and Action Prediction for Generalizable Autonomous Driving in the Long Tail NVIDIA : Wang, Yan Luo, Wenjie Bai, Junjie Cao, Yulong Che, Tong Chen, Ke Chen, Yuxiao Diamond, Jenna Ding, Yifan Ding, Wenhao Feng, Liang Heinrich, Greg Huang, Jack Karkus, Peter Li, Boyi Li, Pinyi Lin, Tsung-Yi Liu, Dongran Liu, Ming-Yu Liu, Langechuan Liu, Zhijian Lu, Jason Mao, Yunxiang Molchanov, Pavlo Pavao, Lindsey Peng, Zhenghao Ranzinger, Mike Schmerling, Ed Shen, Shida Shi, Yunfei Tariq, Sarah Tian, Ran Wekel, Tilman Weng, Xinshuo Xiao, Tianjun Yang, Eric Yang, Xiaodong You, Yurong Zeng, Xiaohui Zhang, Wenyuan Ivanovic, Boris Pavone, Marco Robotics Artificial Intelligence Machine Learning End-to-end architectures trained via imitation learning have advanced autonomous driving by scaling model size and data, yet performance remains brittle in safety-critical long-tail scenarios where supervision is sparse and causal understanding is limited. We introduce Alpamayo-R1 (AR1), a vision-language-action model (VLA) that integrates Chain of Causation reasoning with trajectory planning for complex driving scenarios. Our approach features three key innovations: (1) the Chain of Causation (CoC) dataset, built through a hybrid auto-labeling and human-in-the-loop pipeline producing decision-grounded, causally linked reasoning traces aligned with driving behaviors; (2) a modular VLA architecture combining Cosmos-Reason, a vision-language model pre-trained for Physical AI, with a diffusion-based trajectory decoder that generates dynamically feasible trajectories in real time; (3) a multi-stage training strategy using supervised fine-tuning to elicit reasoning and reinforcement learning (RL) to enforce reasoning-action consistency and optimize reasoning quality. AR1 achieves up to a 12% improvement in planning accuracy on challenging cases compared to a trajectory-only baseline, with a 35% reduction in close encounter rate in closed-loop simulation. RL post-training improves reasoning quality by 45% and reasoning-action consistency by 37%. Model scaling from 0.5B to 7B parameters shows consistent improvements. On-vehicle road tests confirm real-time performance (99 ms latency) and successful urban deployment. By bridging interpretable reasoning with precise control, AR1 demonstrates a practical path towards Level 4 autonomous driving. Model weights are available at https://huggingface.co/nvidia/Alpamayo-R1-10B with inference code at https://github.com/NVlabs/alpamayo. |
| title | Alpamayo-R1: Bridging Reasoning and Action Prediction for Generalizable Autonomous Driving in the Long Tail |
| topic | Robotics Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2511.00088 |