Trajectory-Aware Post-Training of Open-Weight Models for Security Agents

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Presented at [un]prompted 2026 by

Aaron Brown

Madhur Prashant

Everyone talks about AI agents for security, but almost no one talks about how to post-train the underlying open-weight models that power them. Frontier APIs work for prototypes, but scaling autonomous security operations requires fine-tuned small language models optimized for your specific tooling, reasoning patterns, and operational constraints. This talk presents a complete open-source pipeline for trajectory-aware post-training of open-weight SLMs for cybersecurity tasks covering environment setup, data collection and refinement, reward function design, and a two-stage SFT to GRPO training recipe running on NVIDIA DGX Spark. We'll release training configs, the evaluation harness, and fine-tuned GLM-4.7 30B Flash weights on HuggingFace.