Wei Liu, Jiawei Xu, Yingru Li, Longtao Zheng, Tianjian Li, Qian Liu, Junxian He
The paper presents Dr.Kernel, a reinforcement learning approach for generating high-quality AI kernels using a new environment, KernelGYM, achieving significant speedup over existing models.
The research introduces Dr.Kernel, an AI model designed to generate efficient computer code, known as kernels, which are crucial for AI systems to run faster and more effectively. To develop Dr.Kernel, the researchers created a special testing environment called KernelGYM, which helps train the model by simulating real-world scenarios and checking for common training issues. The model was trained using advanced techniques to ensure it learns to prioritize meaningful improvements in speed rather than just superficial correctness. The results show that Dr.Kernel performs better than existing models, achieving faster code execution in a significant number of cases.
