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Expert-vetted reasoning datasets for reinforcement learning: why they lift model performance

Reinforcement learning (RL) is great at learning what to do when the reward signal is clean and the environment is forgiving. But many real-world settings aren’t like that. They’re messy, high-stakes, and full of “almost right” decisions. That’s where expert-vetted reasoning datasets become a force multiplier: they teach models the why behind an action—not just

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How to Build Multi-Layered LLM Safety Filters to Defend Against Adaptive, Paraphrased, and Adversarial Prompt Attacks

In this tutorial, we build a robust, multi-layered safety filter designed to defend large language models against adaptive and paraphrased attacks. We combine semantic similarity analysis, rule-based pattern detection, LLM-driven intent classification, and anomaly detection to create a defense system that relies on no single point of failure. Also, we demonstrate how practical, production-style safety

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