Distilling Knowledge from Large Language Models into Lightweight Reinforcement Learning Agents for Autonomous Cyber Operations
An 8B-parameter cybersecurity-pretrained LLM outperforms baseline RL agents in autonomous cyber defense via prompt engineering alone, without fine-tuning Online policy distillation transfers the LLM's defensive policy into a 64,910-parameter lightweight RL agent, reducing model size by several orders of magnitude The approach was evaluated across CybORG scenarios with 4 to 12 hosts, demonstrating transferability across varying network configurations Teacher-guided RL stabilization strategies wer
Analysis
TL;DR
- An 8B-parameter cybersecurity-pretrained LLM outperforms baseline RL agents in autonomous cyber defense via prompt engineering alone, without fine-tuning
- Online policy distillation transfers the LLM's defensive policy into a 64,910-parameter lightweight RL agent, reducing model size by several orders of magnitude
- The approach was evaluated across CybORG scenarios with 4 to 12 hosts, demonstrating transferability across varying network configurations
- Teacher-guided RL stabilization strategies were tested but none consistently surpassed the optimized teacher policy, revealing policy-alignment gaps between reward-driven RL and teacher-guided strategies
- The work establishes a practical pathway for operationalizing frontier cybersecurity LLMs within efficient, deployable autonomous agents
Why It Matters
This research addresses a critical bottleneck in autonomous cyber defense: RL agents suffer from unstable early training and poor initial decisions, making them risky for real-world deployment. By showing that LLMs can serve as high-quality policy teachers and that their knowledge can be compressed into tiny RL agents, the work bridges the gap between powerful but impractical frontier models and lightweight systems suitable for production cybersecurity operations.
Technical Details
- Teacher model: An 8-billion parameter LLM pretrained on cybersecurity data, used via prompt engineering (no fine-tuning) to generate defensive policies in a modified CybORG CAGE Challenge 2 environment
- Student model: A lightweight RL agent with only 64,910 parameters, trained through online policy distillation from the LLM teacher, achieving a reduction of several orders of magnitude in model size
- Evaluation framework: CybORG simulation environment with constructed scenarios spanning 4 to 12 hosts, testing transferability across varying network topologies and configurations
- Stabilization strategies: Teacher-guided RL stabilization methods were evaluated but found to consistently underperform the raw optimized teacher policy, indicating fundamental misalignment between reward-driven RL optimization objectives and the defensive strategies encoded in the LLM
- Key technical insight: The policy-alignment limitation suggests that direct distillation without stabilization may be more effective than stabilization-augmented approaches, pointing to a design trade-off between training stability and policy fidelity
Industry Insight
- The prompt-engineering-only approach for the LLM teacher significantly lowers the barrier to deploying specialized cybersecurity models, eliminating the need for expensive domain-specific fine-tuning
- The dramatic model size reduction (8B → 64K parameters) makes real-time deployment on resource-constrained edge infrastructure feasible, which is critical for distributed enterprise network defense
- The observed policy-alignment gap between RL reward optimization and LLM-derived strategies suggests that hybrid approaches combining both paradigms—rather than pure distillation—may be needed for optimal autonomous cyber defense systems
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