自动驾驶团队终于有了兼顾安全与意图的强化学习方案——CoPhy用蒸馏VLM和BEV世界模型解决了行为克隆的瓶颈，做端到端驾驶的开发者可以直接参考其双奖励机制。
当前端到端自动驾驶模型受限于模仿学习的行为克隆天花板，强化学习虽能实现更智能的自主性，但缺乏认知基础和前瞻性物理环境。为此，研究者提出CoPhy框架，通过蒸馏VLM知识到BEV编码器，在零推理成本下保留认知能力，并构建自回归BEV世界模型预测未来语义地图，作为可解释的物理沙盒。该框架采用GRPO优化策略，结合物理奖励（确保硬安全约束）和认知奖励（确保意图合规），在NAVSIM v1和v2基准上达到最先进结果，并支持用户自定义语言指令实现灵活意图控制。
Distill to Think, Foresee to Act: Cognitive-Physical Reinforcement Learning for Autonomous Driving
Current end-to-end autonomous driving models are fundamentally constrained by the behavioral cloning ceiling of imitation learning. While reinforcement learning offers a path to smarter autonomy, it demands two missing pieces of infrastructure: (1) a cognitive foundation that understands traffic semantics and driving intent, and (2) a foresighted physical environment that can anticipate the consequences of candidate actions. To this end, we propose CoPhy, a CognitivePhysical reinforcement learning framework for autonomous driving. To distill to think, we distill VLM knowledge into the BEV encoder and then discard the VLM entirely, retaining cognitive ability at zero inference cost while releasing the cognitive channel as a pluggable interface for optional human language commands. To foresee to act, we build an auto-regressive BEV world model that explicitly predicts future semantic maps conditioned on candidate actions, serving as an interpretable physical sandbox from which safety metrics are directly derived. Built upon this dual infrastructure, we optimize the driving policy via GRPO with a novel dual-reward mechanism: a physical reward derived from BEV rollouts enforces hard safety constraints, while a cognitive reward from a language-aligned scorer ensures intent compliance. Extensive experiments demonstrate that CoPhy not only achieves state-of-the-art results on NAVSIM v1 and v2 benchmarks, but also enables safer driving via cognitively informed scene compliance and flexible intent control through user-defined language instructions.