TuneAgent: Agentic Operating System Kernel Tuning with Reinforcement Learning

Understand

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.