用强化学习让大模型自动调优内核，提升系统性能
TuneAgent: Agentic Operating System Kernel Tuning with Reinforcement Learning
- 基于规则的强化学习框架，让大模型安全探索内核配置
- 在真实场景中实现最高5.6%的性能提升，配置正确率高
- 适合系统优化、自动化运维人员，尤其关注性能调优者
Linux内核调优对操作系统性能优化至关重要，但因内核空间复杂、性能反馈稀疏且负载敏感而困难重重。我们提出TuneAgent，一个基于规则强化学习的智能内核调优框架。将内核空间建模为受约束的强化学习环境，使大语言模型（LLMs）能自主探索并执行合法精确的配置修改。针对性能反馈稀疏问题，设计结构化奖励函数，同时促进推理标准化、配置正确性和性能感知。提出两阶段训练策略：先确保格式与语义正确，再转入性能驱动探索，加速收敛并降低开销。实验表明，TuneAgent持续优于现有基线，在多个真实应用中实现最高5.6%的相对性能提升，且配置有效性高。其在多种部署环境中的鲁棒性验证了实际可用性。
原文摘要 · Abstract (English)
Linux kernel tuning is essential for optimizing operating system (OS) performance, yet remains challenging due to the complex kernel space, sparse performance feedback, and strong workload sensitivity. We present TuneAgent, an agentic Linux kernel tuning framework powered by rule-based reinforcement learning (RL). TuneAgent formulates the kernel space as a constrained RL environment, enabling large language models (LLMs) to autonomously explore the kernel while enforcing valid and precise configuration modifications. To address sparse performance feedback, we design structured reward functions that jointly promote reasoning standardization, configuration correctness, and performance awareness. Furthermore, we propose a two-phase training strategy that first ensures format and semantic correctness and then transitions to performance-driven exploration, accelerating convergence and reducing overhead. Experimental results show that TuneAgent consistently outperforms existing baselines, achieving up to 5.6% relative overall performance improvement while maintaining high configuration validity. We further demonstrate its robustness across multiple real-world applications, highlighting its practicality and adaptability in diverse deployment environments.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。