Chris Shum

CUDA-L1: Improving CUDA Optimization via Contrastive Reinforcement Learning

The exponential growth in demand for GPU computing resources has created an urgent need for automated CUDA optimization strategies. While recent advances in LLMs show promise for code generation, current state-of-the-art models achieve low success rates in improving CUDA speed. In this paper, we in…