# ORAN-GUIDE: RAG-Driven Prompt Learning for LLM-Augmented Reinforcement Learning in O-RAN Network Slicing

arXiv: https://arxiv.org/abs/2506.00576 · HTML: https://mlsanity.com/paper/2506.00576/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 31 references that are in the map.
- First posted on arXiv 31 May 2025.

## Builds on (most cited first, 24 of 24)

- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Parameter-Efficient Transfer Learning for NLP](https://mlsanity.com/paper/1902.00751/) (2019)
- [Prefix-Tuning: Optimizing Continuous Prompts for Generation](https://mlsanity.com/paper/2101.00190/) (2021)
- [The Power of Scale for Parameter-Efficient Prompt Tuning](https://mlsanity.com/paper/2104.08691/) (2021)
- [Large Generative AI Models for Telecom: The Next Big Thing?](https://mlsanity.com/paper/2306.10249/) (2023)
- [WirelessLLM: Empowering Large Language Models Towards Wireless Intelligence](https://mlsanity.com/paper/2405.17053/) (2024)
- [Resource Management in Wireless Networks via Multi-Agent Deep Reinforcement Learning](https://mlsanity.com/paper/2002.06215/) (2020)
- [Controlling Large Language Model-based Agents for Large-Scale Decision-Making: An Actor-Critic Approach](https://mlsanity.com/paper/2311.13884/) (2023)
- [Interactive AI with Retrieval-Augmented Generation for Next Generation Networking](https://mlsanity.com/paper/2401.11391/) (2024)
- [Understanding Telecom Language Through Large Language Models](https://mlsanity.com/paper/2306.07933/) (2023)
- [LLM-Empowered State Representation for Reinforcement Learning](https://mlsanity.com/paper/2407.13237/) (2024)
- [ORAN-Bench-13K: An Open Source Benchmark for Assessing LLMs in Open Radio Access Networks](https://mlsanity.com/paper/2407.06245/) (2024)
- [Federated Deep Reinforcement Learning for Resource Allocation in O-RAN Slicing](https://mlsanity.com/paper/2208.01736/) (2022)
- [LLM-Based Intent Processing and Network Optimization Using Attention-Based Hierarchical Reinforcement Learning](https://mlsanity.com/paper/2406.06059/) (2024)
- [TelecomRAG: Taming Telecom Standards with Retrieval Augmented Generation and LLMs](https://mlsanity.com/paper/2406.07053/) (2024)
- [ORANSight-2.0: Foundational LLMs for O-RAN](https://mlsanity.com/paper/2503.05200/) (2025)
- [Evolutionary Deep Reinforcement Learning for Dynamic Slice Management in O-RAN](https://mlsanity.com/paper/2208.14394/) (2022)
- [Meta Reinforcement Learning Approach for Adaptive Resource Optimization in O-RAN](https://mlsanity.com/paper/2410.03737/) (2024)
- [Attention-based Open RAN Slice Management using Deep Reinforcement Learning](https://mlsanity.com/paper/2306.09490/) (2023)
- [ENWAR: A RAG-empowered Multi-Modal LLM Framework for Wireless Environment Perception](https://mlsanity.com/paper/2410.18104/) (2024)
- [Communication and Control Co-Design in 6G: Sequential Decision-Making with LLMs](https://mlsanity.com/paper/2407.06227/) (2024)
- [Open RAN LSTM Traffic Prediction and Slice Management using Deep Reinforcement Learning](https://mlsanity.com/paper/2401.06922/) (2024)
- [Retrieval-Augmented Generation for Mobile Edge Computing via Large Language Model](https://mlsanity.com/paper/2412.20820/) (2024)
- [An Autonomous Network Orchestration Framework Integrating Large Language Models with Continual Reinforcement Learning](https://mlsanity.com/paper/2502.16198/) (2025)

## Built on by (most cited first, 3 of 3)

- [MobiLLM: An Agentic AI Framework for Closed-Loop Threat Mitigation in 6G Open RANs](https://mlsanity.com/paper/2509.21634/) (2025)
- [Task Specific Sharpness Aware O-RAN Resource Management using Multi Agent Reinforcement Learning](https://mlsanity.com/paper/2511.15002/) (2025)
- [TeleEmbedBench: A Multi-Corpus Embedding Benchmark for RAG in Telecommunications](https://mlsanity.com/paper/2604.17778/) (2026)

## Abstract (arXiv)

Advanced wireless networks must support highly dynamic and heterogeneous service demands. Open Radio Access Network (O-RAN) architecture enables this flexibility by adopting modular, disaggregated components, such as the RAN Intelligent Controller (RIC), Centralized Unit (CU), and Distributed Unit (DU), that can support intelligent control via machine learning (ML). While deep reinforcement learning (DRL) is a powerful tool for managing dynamic resource allocation and slicing, it often struggles to process raw, unstructured input like RF features, QoS metrics, and traffic trends. These limitations hinder policy generalization and decision efficiency in partially observable and evolving environments. To address this, we propose textitORAN-GUIDE, a dual-LLM framework that enhances multi-agent RL (MARL) with task-relevant, semantically enriched state representations. The architecture employs a domain-specific language model, ORANSight, pretrained on O-RAN control and configuration data, to generate structured, context-aware prompts. These prompts are fused with learnable tokens and passed to a frozen GPT-based encoder that outputs high-level semantic representations for DRL agents. This design adopts a retrieval-augmented generation (RAG) style pipeline tailored for technical decision-making in wireless systems. Experimental results show that ORAN-GUIDE improves sample efficiency, policy convergence, and performance generalization over standard MARL and single-LLM baselines.