CUDA Agent: Large-Scale Agentic RL for High-Performance CUDA Kernel Generation
GPU kernel optimization is fundamental to modern deep learning but remains a highly specialized task requiring deep hardware expertise. Despite strong performance in general programming, large language models (LLMs) remain uncompetitive with compiler-based systems such as torch.compile for CUDA kernel generation. Existing CUDA code generation approaches either rely on training-free refinement or fine-tune models within fixed multi-turn execution-feedback loops, but both paradigms fail to fundamentally improve the model's intrinsic CUDA optimization ability, resulting in limited performance gains. We present CUDA Agent, a large-scale agentic reinforcement learning system that develops CUDA kernel expertise through three components: a scalable data synthesis pipeline, a skill-augmented CUDA development environment with automated verification and profiling to provide reliable reward signals, and reinforcement learning algorithmic techniques enabling stable training. CUDA Agent achieves state-of-the-art results on KernelBench, delivering 100\%, 100\%, and 92\% faster rate over torch.compile on KernelBench Level-1, Level-2, and Level-3 splits, outperforming the strongest proprietary models such as Claude Opus 4.5 and Gemini 3 Pro by about 40\% on the hardest Level-3 setting.
69a5073ebc2d66e68888f541
Weinan Dai
69a5073ebc2d66e68888f542
Hanlin Wu
69a5073ebc2d66e68888f543
Qiying Yu
69a5073ebc2d66e68888f544
Huan-ang Gao
69a5073ebc2d66e68888f545
Jiahao Li
69a5073ebc2d66e68888f546
Chengquan Jiang
69a5073ebc2d66e68888f547
Weiqiang Lou
69a5073ebc2d66e68888f548
Yufan Song
69a5073ebc2d66e68888f549
Hongli Yu
69a5073ebc2d66e68888f54a
Jiaze Chen
69a5073ebc2d66e68888f54b
Wei-Ying Ma
69a5073ebc2d66e68888f54c
Ya-Qin Zhang
69a5073ebc2d66e68888f54d
Jingjing Liu
69a5073ebc2d66e68888f54e
Mingxuan Wang
69a5073ebc2d66e68888f54f
Xin Liu
69a5073ebc2d66e68888f550
Hao Zhou
CUDA Agent: Large-Scale Agentic RL for High-Performance CUDA Kernel Generation
GPU kernel optimization is fundamental to modern deep learning but remains a highly specialized task requiring deep hardware expertise. Despite strong performance in general programming, large language models (LLMs) remain uncompetitive with compiler-based systems such as torch.compile for CUDA kernel generation. Existing CUDA code generation approaches either rely on training-free refinement or fine-tune models within fixed multi-turn execution-feedback loops, but both paradigms fail to fundamentally improve the model's intrinsic CUDA optimization ability, resulting in limited performance gains. We present CUDA Agent, a large-scale agentic reinforcement learning system that develops CUDA kernel expertise through three components: a scalable data synthesis pipeline, a skill-augmented CUDA development environment with automated verification and profiling to provide reliable reward signals, and reinforcement learning algorithmic techniques enabling stable training. CUDA Agent achieves state-of-the-art results on KernelBench, delivering 100\%, 100\%, and 92\% faster rate over torch.compile on KernelBench Level-1, Level-2, and Level-3 splits, outperforming the strongest proprietary models such as Claude Opus 4.5 and Gemini 3 Pro by about 40\% on the hardest Level-3 setting.
CUDA Agent, a large-scale agentic reinforcement learning system, achieves state-of-the-art performance in CUDA kernel optimization by combining scalable data synthesis, skill-augmented development environment, and reinforcement learning techniques.
CUDA kernel optimization large language models torch.compile reinforcement learning kernel generation CUDA development environment automated verification profiling reward signals KernelBench