RationAnomaly: Log Anomaly Detection with Rationality via Chain-of-Thought and Reinforcement Learning

• URL: http://arxiv.org/abs/2509.14693v2
• Date: Mon, 22 Sep 2025 02:54:39 GMT
• Title: RationAnomaly: Log Anomaly Detection with Rationality via Chain-of-Thought and Reinforcement Learning
• Abstract summary: RationAnomaly is a novel framework that enhances log anomaly detection by synergizing Chain-of-Thought fine-tuning with reinforcement learning.<n>We have released the corresponding resources, including code and datasets.
• Score: 27.235259453535537
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Logs constitute a form of evidence signaling the operational status of software systems. Automated log anomaly detection is crucial for ensuring the reliability of modern software systems. However, existing approaches face significant limitations: traditional deep learning models lack interpretability and generalization, while methods leveraging Large Language Models are often hindered by unreliability and factual inaccuracies. To address these issues, we propose RationAnomaly, a novel framework that enhances log anomaly detection by synergizing Chain-of-Thought (CoT) fine-tuning with reinforcement learning. Our approach first instills expert-like reasoning patterns using CoT-guided supervised fine-tuning, grounded in a high-quality dataset corrected through a rigorous expert-driven process. Subsequently, a reinforcement learning phase with a multi-faceted reward function optimizes for accuracy and logical consistency, effectively mitigating hallucinations. Experimentally, RationAnomaly outperforms state-of-the-art baselines, achieving superior F1-scores on key benchmarks while providing transparent, step-by-step analytical outputs. We have released the corresponding resources, including code and datasets.

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