arXiv:2508.12551v2 Announce Type: replace Abstract: 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 perform
The increasing complexity of operating systems and AI models necessitates advanced autonomous tuning solutions, coinciding with rapid advancements in reinforcement learning and large language models.
This breakthrough provides a framework for autonomous optimization of critical infrastructure, potentially leading to significant performance gains and reduced operational overhead in various AI and computing applications.
Operating system kernels can now be managed and optimized more dynamically and autonomously by AI agents, reducing the reliance on manual expert tuning and improving system efficiency.
- · Cloud providers
- · Data centers
- · AI infrastructure developers
- · Linux ecosystem
- · Manual kernel tuners
- · Companies without AI-driven optimization
System performance across various computational workloads will see measurable improvements due to optimized kernel configurations.
The cost of operating and maintaining large-scale computing infrastructure could decrease as AI agents automate complex tuning tasks.
This technology could accelerate the development and deployment of more complex AI systems by providing a foundation of highly optimized underlying compute.
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Read at arXiv cs.LG