Reasoning Language Models for Root Cause Analysis in 5G Wireless Networks

TL;DR

This paper introduces a domain-specific, reasoning-enhanced approach using large language models for root cause analysis in 5G networks, demonstrating improved interpretability and accuracy through a novel training methodology and curated dataset.

Contribution

It presents a two-stage training method combining supervised fine-tuning and reinforcement learning to adapt LLMs for effective RCA in 5G networks, along with a new benchmark dataset TeleLogs.

Findings

Significant performance improvements over existing models.

Enhanced interpretability and diagnostic explanation quality.

Strong generalization to test variants.

Abstract

Root Cause Analysis (RCA) in mobile networks remains a challenging task due to the need for interpretability, domain expertise, and causal reasoning. In this work, we propose a lightweight framework that leverages Large Language Models (LLMs) for RCA. To do so, we introduce TeleLogs, a curated dataset of annotated troubleshooting problems designed to benchmark RCA capabilities. Our evaluation reveals that existing open-source reasoning LLMs struggle with these problems, underscoring the need for domain-specific adaptation. To address this issue, we propose a two-stage training methodology that combines supervised fine-tuning with reinforcement learning to improve the accuracy and reasoning quality of LLMs. The proposed approach fine-tunes a series of RCA models to integrate domain knowledge and generate structured, multi-step diagnostic explanations, improving both interpretability and effectiveness. Extensive experiments across multiple LLM sizes show significant performance gains over state-of-the-art reasoning and non-reasoning models, including strong generalization to randomized test variants. These results demonstrate the promise of domain-adapted, reasoning-enhanced LLMs for practical and explainable RCA in network operation and management.