关于理想VLA未来发展的一些信息

Core Viewpoint - The article discusses the future of Li Auto's VLA (Vehicle Learning Architecture), emphasizing the development of a reinforcement learning closed loop by the end of 2025, which is expected to significantly enhance user experience and vehicle performance [2][3]. Short-term Outlook - Li Auto aims to establish a reinforcement learning closed loop by the end of 2025, with expectations of noticeable improvements in vehicle performance and user perception by early 2026 [2]. Mid-term Outlook - After strengthening the reinforcement learning closed loop, Li Auto anticipates surpassing Tesla in the Chinese market due to its unique advantages in closed-loop iteration [3]. - The transformation brought by VLA's reinforcement learning is seen as a significant business change, creating a true competitive moat for the company, which will take 1-2 years to fully implement [3]. Long-term Outlook - VLA is projected to achieve Level 4 autonomy, but new technologies are expected to emerge beyond this [4]. - Current safety restrictions are in place to mitigate risks, with the system designed to autonomously identify and address issues through data collection and training [4]. Key Insights on VLA - Li Auto's leadership believes that the intelligence required for driving is relatively low, and after business process reforms, the computational needs for vehicle performance will not be excessively high [5][6]. - The company is focusing on a balanced computational requirement of around 1000 to 2000 TOPS for vehicles and 32 billion for cloud processing [6]. Organizational Adjustments - Li Auto's autonomous driving department is undergoing structural changes to enhance its business system rather than relying on individual talents, with a focus on AI-oriented organization [12]. - The restructuring includes splitting existing teams into specialized departments to improve efficiency and innovation [12]. Competitive Landscape - Li Auto's approach to VLA has faced skepticism from competitors, but the company views this as validation of its strategy [14]. - The article highlights the importance of data quality and distribution in achieving effective autonomous driving, emphasizing the need for human-like reasoning capabilities in systems [18]. Strategic Focus - The company is committed to delivering substantial functional upgrades and user experience improvements on a quarterly basis [18]. - Li Auto's leadership emphasizes the importance of clear communication of company strategy to engage younger employees effectively [18].