- 搭建并维护用于端到端和 VLA 自动驾驶模型的强化学习闭环训练流程。
- 设计和实现支持 RL 闭环训练与评测的仿真环境。
- 开发高效可扩展的工具链，包括数据管理、实验调度和性能监控。
- 对强化学习算法进行优化，提升训练效率、可扩展性及实时部署能力。
- 与研究团队协作，将新的 RL 方法集成到闭环系统中。
- 记录开发流程与基准结果，提供部署相关的技术支持。
- Build and maintain closed-loop reinforcement learning training pipelines for E2E and VLA autonomous driving models.
- Design and implement simulation environments to support RL-based closed-loop training and evaluation.
- Develop scalable toolchains for dataset management, experiment orchestration, and performance monitoring.
- Optimize RL algorithms for efficiency, scalability, and real-time deployment.
- Collaborate with research teams to integrate new RL methods into the closed-loop system.
- Document development workflows, benchmark results, and provide technical support for deployment.
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