# NetLLM: Adapting Large Language Models for Networking

arXiv: https://arxiv.org/abs/2402.02338 · HTML: https://mlsanity.com/paper/2402.02338/

## Key facts

- Cited by 78 papers in the map (data through 14 Oct 2026).
- Builds on 51 of its 111 references that are in the map.
- First posted on arXiv 4 Feb 2024.

## Builds on (most cited first, 30 of 51)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Efficient Memory Management for Large Language Model Serving with PagedAttention](https://mlsanity.com/paper/2309.06180/) (2023)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [Finetuned Language Models Are Zero-Shot Learners](https://mlsanity.com/paper/2109.01652/) (2021)
- [OPT: Open Pre-trained Transformer Language Models](https://mlsanity.com/paper/2205.01068/) (2022)
- [RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control](https://mlsanity.com/paper/2307.15818/) (2023)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021)
- [PaLM-E: An Embodied Multimodal Language Model](https://mlsanity.com/paper/2303.03378/) (2023)
- [Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems](https://mlsanity.com/paper/2005.01643/) (2020)
- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models](https://mlsanity.com/paper/2306.13394/) (2023)
- [Decision Transformer: Reinforcement Learning via Sequence Modeling](https://mlsanity.com/paper/2106.01345/) (2021)
- [SparseGPT: Massive Language Models Can Be Accurately Pruned in One-Shot](https://mlsanity.com/paper/2301.00774/) (2023)
- [Relational Knowledge Distillation](https://mlsanity.com/paper/1904.05068/) (2019)
- [The RefinedWeb Dataset for Falcon LLM: Outperforming Curated Corpora with Web Data, and Web Data Only](https://mlsanity.com/paper/2306.01116/) (2023)
- [Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning](https://mlsanity.com/paper/2012.13255/) (2020)
- [Unifying Large Language Models and Knowledge Graphs: A Roadmap](https://mlsanity.com/paper/2306.08302/) (2023)
- [Offline Reinforcement Learning as One Big Sequence Modeling Problem](https://mlsanity.com/paper/2106.02039/) (2021)
- [Improved Knowledge Distillation via Teacher Assistant](https://mlsanity.com/paper/1902.03393/) (2019)

## Built on by (most cited first, 30 of 78)

- [Large Model Based Agents: State-of-the-Art, Cooperation Paradigms, Security and Privacy, and Future Trends](https://mlsanity.com/paper/2409.14457/) (2024)
- [A Survey on Large Language Models for Communication, Network, and Service Management: Application Insights, Challenges, and Future Directions](https://mlsanity.com/paper/2412.19823/) (2024)
- [Integrating LLMs with ITS: Recent Advances, Potentials, Challenges, and Future Directions](https://mlsanity.com/paper/2501.04437/) (2025)
- [SCALM: Towards Semantic Caching for Automated Chat Services with Large Language Models](https://mlsanity.com/paper/2406.00025/) (2024)
- [Large Language Models for Networking: Workflow, Advances and Challenges](https://mlsanity.com/paper/2404.12901/) (2024)
- [CATP-LLM: Empowering Large Language Models for Cost-Aware Tool Planning](https://mlsanity.com/paper/2411.16313/) (2024)
- [Large Language Models for Wireless Communications: From Adaptation to Autonomy](https://mlsanity.com/paper/2507.21524/) (2025)
- [Incident Response Planning Using a Lightweight Large Language Model with Reduced Hallucination](https://mlsanity.com/paper/2508.05188/) (2025)
- [Agentic AI Empowered Intent-Based Networking for 6G](https://mlsanity.com/paper/2601.06640/) (2026)
- [WirelessAgent++: Automated Agentic Workflow Design and Benchmarking for Wireless Networks](https://mlsanity.com/paper/2603.00501/) (2026)
- [MoE2: Optimizing Collaborative Inference for Edge Large Language Models](https://mlsanity.com/paper/2501.09410/) (2025)
- [LightLLM: A Versatile Large Language Model for Predictive Light Sensing](https://mlsanity.com/paper/2411.15211/) (2024)
- [FOVA: Offline Federated Reinforcement Learning with Mixed-Quality Data](https://mlsanity.com/paper/2512.02350/) (2025)
- [NetAgentBench: A State-Centric Benchmark for Evaluating Agentic Network Configuration](https://mlsanity.com/paper/2604.09678/) (2026)
- [Satellite Edge Artificial Intelligence with Large Models: Architectures and Technologies](https://mlsanity.com/paper/2504.01676/) (2025)
- [Respond to Change with Constancy: Instruction-tuning with LLM for Non-I.I.D. Network Traffic Classification](https://mlsanity.com/paper/2505.20866/) (2025)
- [SADE: Symptom-Aware Diagnostic Escalation for LLM-Based Network Troubleshooting](https://mlsanity.com/paper/2605.04530/) (2026)
- [On-Device LLM for Context-Aware Wi-Fi Roaming](https://mlsanity.com/paper/2505.04174/) (2025)
- [Cost-Efficient LLM Training with Lifetime-Aware Tensor Offloading via GPUDirect Storage](https://mlsanity.com/paper/2506.06472/) (2025)
- [A Satellite-Ground Synergistic Large Vision-Language Model System for Earth Observation](https://mlsanity.com/paper/2507.05731/) (2025)
- [6GAgentGym: Tool Use, Data Synthesis, and Agentic Learning for Network Management](https://mlsanity.com/paper/2603.29656/) (2026)
- [DarwinNet: An Evolutionary Network Architecture for Agent-Driven Protocol Synthesis](https://mlsanity.com/paper/2604.01236/) (2026)
- [Edge Large AI Models: Revolutionizing 6G Networks](https://mlsanity.com/paper/2505.00321/) (2025)
- [AI2MMUM: AI-AI Oriented Multi-Modal Universal Model Leveraging Telecom Domain Large Model](https://mlsanity.com/paper/2505.10003/) (2025)
- [From Large AI Models to Agentic AI: A Tutorial on Future Intelligent Communications](https://mlsanity.com/paper/2505.22311/) (2025)
- [Pegasus: A Universal Framework for Scalable Deep Learning Inference on the Dataplane](https://mlsanity.com/paper/2506.05779/) (2025)
- [Beyond Interpretability: Exploring the Comprehensibility of Adaptive Video Streaming through Large Language Models](https://mlsanity.com/paper/2508.16448/) (2025)
- [Cost and Accuracy of Long-Term Memory in Distributed Multi-Agent Systems Based on Large Language Models](https://mlsanity.com/paper/2601.07978/) (2026)
- [Rollout-Training Co-Design for Efficient LLM-Based Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2602.09578/) (2026)
- [PLUME: Building a Network-Native Foundation Model for Wireless Traces via Protocol-Aware Tokenization](https://mlsanity.com/paper/2603.13647/) (2026)

## Abstract (arXiv)

Many networking tasks now employ deep learning (DL) to solve complex prediction and optimization problems. However, current design philosophy of DL-based algorithms entails intensive engineering overhead due to the manual design of deep neural networks (DNNs) for different networking tasks. Besides, DNNs tend to achieve poor generalization performance on unseen data distributions/environments. Motivated by the recent success of large language models (LLMs), this work studies the LLM adaptation for networking to explore a more sustainable design philosophy. With the powerful pre-trained knowledge, the LLM is promising to serve as the foundation model to achieve "one model for all tasks" with even better performance and stronger generalization. In pursuit of this vision, we present NetLLM, the first framework that provides a coherent design to harness the powerful capabilities of LLMs with low efforts to solve networking problems. Specifically, NetLLM empowers the LLM to effectively process multimodal data in networking and efficiently generate task-specific answers. Besides, NetLLM drastically reduces the costs of fine-tuning the LLM to acquire domain knowledge for networking. Across three networking-related use cases - viewport prediction, adaptive bitrate streaming and cluster job scheduling, we showcase that the NetLLM-adapted LLM significantly outperforms state-of-the-art algorithms.