daVinci-kernel：通过强化学习协同进化技能选择、总结与利用的GPU内核优化
daVinci-kernel: Co-Evolving Skill Selection, Summarization, and Utilization via RL for GPU Kernel Optimization
AI总结：
提出daVinci-kernel框架，通过强化学习联合训练技能选择、策略生成和技能总结三个智能体，共享LLM骨干，实现GPU内核优化，在KernelBench上超越先前最优模型。
摘要：
GPU内核优化代表了一种范式，其中功能正确性被假定，执行效率是目标。我们提出daVinci-kernel，一个强化学习框架，通过动态演化的技能库将技能发现与技能利用相结合。daVinci-kernel联合训练三个共享一个LLM骨干的智能体：技能选择智能体通过BM25和LLM重排序检索相关技术，策略智能体基于所选技能生成多轮CUDA/Triton内核，技能总结智能体将成功轨迹提炼为可复用技能。候选技能仅在基于执行的验证确认可复现加速后才被添加。所有三个智能体共享单个LLM骨干，通过多样性过滤数据上的结构化SFT冷启动初始化，然后通过多轮REINFORCE和每个智能体的优势估计进行端到端联合优化。在KernelBench上，daVinci-kernel-14B在Fast$_1$阈值下，Level 1、Level 2和Level 3分别达到37.2%、70.6%和32.2%，优于先前最强的RL训练模型Dr.Kernel-14B。
英文摘要：
GPU kernel optimization represents a paradigm where functional correctness is assumed and execution efficiency is the objective. We present daVinci-kernel, a reinforcement learning framework that couples skill discovery with skill exploitation through a dynamically evolving skill library. daVinci-kernel jointly trains three agents sharing one LLM backbone: a Skill Selection Agent that retrieves relevant techniques via BM25 and LLM reranking, a Policy Agent that generates multi-turn CUDA/Triton kernels conditioned on selected skills, and a Skill Summary Agent that distills successful rollouts into reusable skills. Candidate skills are added only after execution-based verification confirms reproducible speedups. All three agents share a single LLM backbone, are initialized via a structured SFT cold start on diversity-filtered data, and are then jointly optimized end-to-end with multi-turn REINFORCE and per-agent advantage estimation. On KernelBench, daVinci-kernel-14B achieves 37.2%, 70.6%, and 32.2% on Level 1, Level 2, and Level 3 under the Fast$_1$ threshold, outperforming the strongest prior RL-trained model, Dr\. Kernel-14B.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
