# OpsLLM: Construction of Large Language Model for Software Operations with Multi-stage Learning

arXiv: https://arxiv.org/abs/2605.02906 · HTML: https://mlsanity.com/paper/2605.02906/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 29 of its 49 references that are in the map.
- First posted on arXiv 6 Apr 2026.

## Builds on (most cited first, 29 of 29)

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- [ThinkFL: Self-Refining Failure Localization for Microservice Systems via Reinforcement Fine-Tuning](https://mlsanity.com/paper/2504.18776/) (2025)
- [Assess and Summarize: Improve Outage Understanding with Large Language Models](https://mlsanity.com/paper/2305.18084/) (2023)
- [CodeArena: A Collective Evaluation Platform for LLM Code Generation](https://mlsanity.com/paper/2503.01295/) (2025)
- [Why Do AI Agents Systematically Fail at Cloud Root Cause Analysis?](https://mlsanity.com/paper/2602.09937/) (2026)
- [Easy Dataset: A Unified and Extensible Framework for Synthesizing LLM Fine-Tuning Data from Unstructured Documents](https://mlsanity.com/paper/2507.04009/) (2025)
- [Cross-System Categorization of Abnormal Traces in Microservice-Based Systems via Meta-Learning](https://mlsanity.com/paper/2403.18998/) (2024)

## Abstract (arXiv)

In the field of software operations, Large Language Models (LLMs) have attracted increasing attention. However, existing research has not yet achieved efficient and effective endto-end intelligent operations due to low-quality data, fragmented knowledge and insufficient learning. To explore the potential of LLMs in software operations, we propose OpsLLM, a domainspecific LLM that supports both knowledge-based question answering (QA) and root cause analysis (RCA). Moreover, we disclose the detailed workflow for building LLMs specifically in the software operations domain. First, a Human-in-the-Loop mechanism is introduced to curate high-quality data from a large collection of operational data and construct a fine-tuning dataset. Then, based on the data, supervised fine-tuning is conducted to achieve a base model. Furthermore, we introduce a domain process reward model (DPRM) during the reinforcement learning stage to optimize the accuracy and reliability of the fine-tuned model on RCA tasks. Experimental results on the tasks with diverse difficulties demonstrate that OpsLLMs effectively learns and aligns with the operational domain knowledge infused, outperforming existing open-source and closed-source LLMs in accuracy with improvements of 0.2%~11.9% on QA tasks and 8.5%~70.3% on RCA tasks, while exhibiting strong transferability. Moreover, we will open-source three versions of OpsLLM with 7B, 14B and 32B parameters, along with a 15K fine-tuning dataset.