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[2509.05311] Large Language Model Integration with Reinforcement Learning to Augment Decision-Making in Autonomous Cyber Operations
Summary
This article explores the integration of Large Language Models (LLMs) with Reinforcement Learning (RL) to enhance decision-making in autonomous cyber operations, demonstrating improved performance and faster convergence in training.
Why It Matters
The integration of LLMs with RL represents a significant advancement in cybersecurity, allowing for more efficient learning processes that can reduce the risks associated with trial-and-error methods. This research has implications for developing smarter, more adaptive cybersecurity systems that can better respond to threats.
Key Takeaways
- Combining LLMs with RL can enhance decision-making in cybersecurity.
- The approach reduces the need for risky exploratory actions during training.
- The guided RL agent achieves over 2x higher rewards and faster convergence.
Computer Science > Cryptography and Security arXiv:2509.05311 (cs) [Submitted on 28 Aug 2025 (v1), last revised 13 Feb 2026 (this version, v2)] Title:Large Language Model Integration with Reinforcement Learning to Augment Decision-Making in Autonomous Cyber Operations Authors:Konur Tholl, François Rivest, Mariam El Mezouar, Adrian Taylor, Ranwa Al Mallah View a PDF of the paper titled Large Language Model Integration with Reinforcement Learning to Augment Decision-Making in Autonomous Cyber Operations, by Konur Tholl and 3 other authors View PDF HTML (experimental) Abstract:Reinforcement Learning (RL) has shown great potential for autonomous decision-making in the cybersecurity domain, enabling agents to learn through direct environment interaction. However, RL agents in Autonomous Cyber Operations (ACO) typically learn from scratch, requiring them to execute undesirable actions to learn their consequences. In this study, we integrate external knowledge in the form of a Large Language Model (LLM) pretrained on cybersecurity data that our RL agent can directly leverage to make informed decisions. By guiding initial training with an LLM, we improve baseline performance and reduce the need for exploratory actions with obviously negative outcomes. We evaluate our LLM-integrated approach in a simulated cybersecurity environment, and demonstrate that our guided agent achieves over 2x higher rewards during early training and converges to a favorable policy approximately 4,500 epi...
