CritiqueDriveVLM：从验证器引导的强化学习到自动驾驶的潜在思想蒸馏
CritiqueDriveVLM: From Verifier-Guided Reinforcement Learning to Latent Thought Distillation for Autonomous Driving
AI总结：
提出CritiqueDriveVLM框架解决自动驾驶中推理幻觉等问题，先通过验证器引导的强化学习培养强大教师模型，再用潜在思想蒸馏压缩能力到学生模型，提升性能并大幅降低推理延迟。
摘要：
端到端视觉语言模型在自动驾驶中有巨大潜力，但标准监督微调有问题。传统方法有令牌消耗和延迟问题。我们提出CritiqueDriveVLM，通过验证器引导的强化学习培养教师模型，再用潜在思想蒸馏得到学生模型，在基准测试中有显著改进。
英文摘要：
End-to-end Vision-Language Models (VLMs) show immense potential in autonomous driving. However, standard Supervised Fine-Tuning (SFT) often suffers from reasoning hallucinations and conservative biases. While traditional tool-augmented frameworks and Chain-of-Thought (CoT) approaches mitigate these issues, they incur exorbitant token consumption and unacceptable latency, rendering real-time deployment impractical. To resolve this reliability-efficiency trade-off, we propose CritiqueDriveVLM, a novel unified three-stage framework internalizing reasoning directly into the VLM. First, we introduce Critique-Driven Multi-Turn Reinforcement Learning (RL) guided by a multi-dimensional verifier. By providing granular scalar feedback and a multi-turn penalty, we force the policy to internalize logical deduction, cultivating a robust System-2 Teacher that achieves high accuracy without fragile external tools. Subsequently, we propose Latent Thought Distillation to overcome the latency bottleneck. By aligning the Student's latent representations with the Teacher's fully converged reasoning states, we compress deep logical capabilities into a fast, CoT-free System-1 Student. Extensive experiments on the widely-used DriveLMM-01 benchmark demonstrate remarkable improvements. Compared to the base model, our tool-free Teacher significantly boosts Multiple Choice Quality (MCQ) from 55.54% to a state-of-the-art 76.54%. Crucially, our distilled Student preserves competitive reasoning depth while drastically minimizing generation length to an average of merely 28 tokens. This slashes inference latency by 88% (from 3482 ms to 416 ms), paving a highly robust pathway for low-latency autonomous driving.Our source code is available at https://github.com/MICLAB-BUPT/CritiqueDriveVLM.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
