Hard Preference Sampling (HPS) offers a more effective way to align language models with human preferences, especially for security-critical applications.

• Creates larger reward margins between preferred and harmful content
• More efficiently utilizes negative examples in training
• Demonstrates superior performance on safety benchmarks like PKU-Safety
• Achieves better alignment while being computationally efficient

This research addresses critical security concerns by reducing harmful content generation in LLMs, making AI systems safer and more controllable for real-world deployment.

HPS: Hard Preference Sampling for Human Preference Alignment