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arXiv · 2507.11948

Kevin: Multi-Turn RL for Generating CUDA Kernels

Carlo Baronio·Pietro Marsella·Ben Pan·Simon Guo·Silas Alberti
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Abstract

Writing GPU kernels is a challenging task and critical for AI systems' efficiency. It is also highly iterative: domain experts write code and improve performance through execution feedback. Moreover, it presents verifiable rewards like correctness and speedup, making it a natural environment to apply Reinforcement Learning (RL). To explicitly incorporate the iterative nature of this process into training, we develop a flexible multi-turn RL recipe that addresses unique challenges encountered in real-world settings, such as learning from long trajectories and effective reward attribution across turns. We present Kevin - K(ernel D)evin, the first model trained with multi-turn RL for CUDA kernel generation and optimization. In our evaluation setup, Kevin shows significant gains over its base model (QwQ-32B), improving correctness of generated kernels (in pure CUDA) from 56% to 82% and mean speedup from 0.53x to 1.10x of baseline (PyTorch Eager), and surpassing frontier models like o4-mini (0.78x). Finally, we study its behavior across test-time scaling axes: we found scaling serial refinement more beneficial than parallel sampling. In particular, when given more refinement turns, Kevin shows a higher rate of improvement.

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Carlo Baronio, Pietro Marsella, Ben Pan, Simon Guo, Silas Alberti. 2025-07-16. Kevin: Multi-Turn RL for Generating CUDA Kernels. https://arxiv.org/abs/2507.11948

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