Kevin-32B Uses Multi-Turn Reinforcement Learning to Write Faster CUDA Kernels

Stanford and Cognition AI researchers introduced Kevin-32B, a 32B-parameter model trained with multi-turn reinforcement learning to write CUDA kernels.

On KernelBench, it solves 89% of tasks at best@16 and achieves 65% average correctness over eight refinement steps, versus 53% for o4-mini and 51% for o3.

Its best@16 speedup is 1.41x, and multi-turn training outperforms single-turn training as refinement steps increase.