# TuneAgent: Agentic Operating System Kernel Tuning with Reinforcement Learning

arXiv: https://arxiv.org/abs/2508.12551 · HTML: https://mlsanity.com/paper/2508.12551/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use.
- Builds on 10 of its 32 references that are in the map.
- First posted on arXiv 18 Aug 2025.

## Placed in

- LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/)

## Builds on (most cited first, 10 of 10)

- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning](https://mlsanity.com/paper/2503.09516/) (2025)
- [GUI-R1 : A Generalist R1-Style Vision-Language Action Model For GUI Agents](https://mlsanity.com/paper/2504.10458/) (2025)
- [Light-R1: Curriculum SFT, DPO and RL for Long COT from Scratch and Beyond](https://mlsanity.com/paper/2503.10460/) (2025)
- [Ego-R1: Chain-of-Tool-Thought for Ultra-Long Egocentric Video Reasoning](https://mlsanity.com/paper/2506.13654/) (2025)
- [Drive-R1: Bridging Reasoning and Planning in VLMs for Autonomous Driving with Reinforcement Learning](https://mlsanity.com/paper/2506.18234/) (2025)
- [Scene-R1: Video-Grounded Large Language Models for 3D Scene Reasoning without 3D Annotations](https://mlsanity.com/paper/2506.17545/) (2025)
- [ConfigFix: Interactive Configuration Conflict Resolution for the Linux Kernel](https://mlsanity.com/paper/2012.15342/) (2020)
- [BYOS: Knowledge-driven Large Language Models Bring Your Own Operating System More Excellent](https://mlsanity.com/paper/2503.09663/) (2025)
- [Autonomous Configuration of Network Parameters in Operating Systems using Evolutionary Algorithms](https://mlsanity.com/paper/1808.10733/) (2018)

## Built on by (most cited first, 5 of 5)

- [The Landscape of Agentic Reinforcement Learning for LLMs: A Survey](https://mlsanity.com/paper/2509.02547/) (2025)
- [A Survey of Reinforcement Learning for Large Reasoning Models](https://mlsanity.com/paper/2509.08827/) (2025)
- [Hierarchy-of-Groups Policy Optimization for Long-Horizon Agentic Tasks](https://mlsanity.com/paper/2602.22817/) (2026)
- [A Survey on Agentic Multimodal Large Language Models](https://mlsanity.com/paper/2510.10991/) (2025)
- [A Case for Agentic Tuning: From Documentation to Action in PostgreSQL](https://mlsanity.com/paper/2605.19988/) (2026)

## Abstract (arXiv)

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.