Kernel-Smith: A Unified Recipe for Evolutionary Kernel Optimization
He Du, Qiming Ge, Jiakai Hu, Aijun Yang, Zheng Cai, Zixian Huang, Sheng Yuan, Qinxiu Cheng, Xinchen Xie, Yicheng Chen, Yining Li, Jiaxing Xie, Huanan Dong, Yaguang Wu, Xiangjun Huang, Jian Yang, Hui Wang, Bowen Zhou, Bowen Li, Qipeng Guo, Kai Chen

TL;DR
Kernel-Smith is a GPU kernel optimization framework that combines evolutionary algorithms with reinforcement learning to achieve state-of-the-art performance across multiple platforms.
Contribution
It introduces a unified evolutionary and RL-based approach for high-performance GPU kernel generation, surpassing existing models on benchmark and production systems.
Findings
Achieves top speedup ratios on KernelBench with Nvidia Triton backend.
Outperforms proprietary models like Gemini-3.0-pro and Claude-4.6-opus.
Successfully adapts across heterogeneous platforms such as MetaX MACA.
Abstract
We present Kernel-Smith, a framework for high-performance GPU kernel and operator generation that combines a stable evaluation-driven evolutionary agent with an evolution-oriented post-training recipe. On the agent side, Kernel-Smith maintains a population of executable candidates and iteratively improves them using an archive of top-performing and diverse programs together with structured execution feedback on compilation, correctness, and speedup. To make this search reliable, we build backend-specific evaluation services for Triton on NVIDIA GPUs and Maca on MetaX GPUs. On the training side, we convert long-horizon evolution trajectories into step-centric supervision and reinforcement learning signals by retaining correctness-preserving, high-gain revisions, so that the model is optimized as a strong local improver inside the evolutionary loop rather than as a one-shot generator.…
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