title: AlphaDrive: Unleashing the Power of VLMs in Autonomous Driving via
    Reinforcement Learning and Reasoning
summary: OpenAI o1 and DeepSeek R1 achieve or even surpass human expert-level
  performance in complex domains like mathematics and science, with reinforcement
  learning (RL) and reasoning playing a crucial role. In autonomous driving,
  recent end-to-end models have greatly improved planning performance but still
  struggle with long-tailed problems due to limited common sense and reasoning
  abilities. Some studies integrate vision-language models (VLMs) into autonomous
  driving, but they typically rely on pre-trained models with simple supervised
  fine-tuning (SFT) on driving data, without further exploration of training
  strategies or optimizations specifically tailored for planning. In this paper,
  we propose AlphaDrive, a RL and reasoning framework for VLMs in autonomous
  driving. AlphaDrive introduces four GRPO-based RL rewards tailored for planning
  and employs a two-stage planning reasoning training strategy that combines SFT
  with RL. As a result, AlphaDrive significantly improves both planning
  performance and training efficiency compared to using only SFT or without
  reasoning. Moreover, we are also excited to discover that, following RL
  training, AlphaDrive exhibits some emergent multimodal planning capabilities,
  which is critical for improving driving safety and efficiency. To the best of
  our knowledge, AlphaDrive is the first to integrate GRPO-based RL with planning
  reasoning into autonomous driving. Code will be released to facilitate future
  research.
paper_authors._id: 67cfa5bcb17ca92d24da9033
paper_authors.name: Bo Jiang
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paper_authors.fullname: Bo Jiang
paper_authors.type: user
paper_authors.user: rb93dett
paper_authors._id: 67cfa5bcb17ca92d24da9034
paper_authors.name: Shaoyu Chen
paper_authors.status: admin_assigned
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paper_authors.fullname: Shaoyu Chen
paper_authors.type: user
paper_authors.user: Atan-0221
paper_authors._id: 67cfa5bcb17ca92d24da9035
paper_authors.name: Qian Zhang
paper_authors._id: 67cfa5bcb17ca92d24da9036
paper_authors.name: Wenyu Liu
paper_authors.status: admin_assigned
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paper_authors.fullname: LWY
paper_authors.type: user
paper_authors.user: wenyuliu
paper_authors._id: 67cfa5bcb17ca92d24da9037
paper_authors.name: Xinggang Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-11T11:16:56.180Z
paper_authors._id: 62600de6d47e3dbae32ce1ce
paper_authors.avatarUrl: /avatars/a536417cfec6e10ac415091bd1829426.svg
paper_authors.fullname: Xinggang Wang
paper_authors.type: user
paper_authors.user: xinggangw
paper_title: AlphaDrive: Unleashing the Power of VLMs in Autonomous Driving via
    Reinforcement Learning and Reasoning
paper_summary: OpenAI o1 and DeepSeek R1 achieve or even surpass human expert-level
  performance in complex domains like mathematics and science, with reinforcement
  learning (RL) and reasoning playing a crucial role. In autonomous driving,
  recent end-to-end models have greatly improved planning performance but still
  struggle with long-tailed problems due to limited common sense and reasoning
  abilities. Some studies integrate vision-language models (VLMs) into autonomous
  driving, but they typically rely on pre-trained models with simple supervised
  fine-tuning (SFT) on driving data, without further exploration of training
  strategies or optimizations specifically tailored for planning. In this paper,
  we propose AlphaDrive, a RL and reasoning framework for VLMs in autonomous
  driving. AlphaDrive introduces four GRPO-based RL rewards tailored for planning
  and employs a two-stage planning reasoning training strategy that combines SFT
  with RL. As a result, AlphaDrive significantly improves both planning
  performance and training efficiency compared to using only SFT or without
  reasoning. Moreover, we are also excited to discover that, following RL
  training, AlphaDrive exhibits some emergent multimodal planning capabilities,
  which is critical for improving driving safety and efficiency. To the best of
  our knowledge, AlphaDrive is the first to integrate GRPO-based RL with planning
  reasoning into autonomous driving. Code will be released to facilitate future
  research.
paper_ai_keywords: reinforcement learning (RL) reasoning end-to-end models vision-language models (VLMs) supervised fine-tuning (SFT) GRPO-based RL rewards two-stage planning reasoning training strategy emergent multimodal planning capabilities