This research proposes Equilibrate RLHF, a new methodology for training language models that optimally balances safety guardrails with helpful responses.

• Identifies inherent conflicts between safety and helpfulness in standard RLHF approaches
• Introduces a specialized training framework that prevents safety degradation during helpfulness optimization
• Demonstrates empirically that balanced training yields better security outcomes without sacrificing utility
• Provides a practical pathway for developing LLMs that remain both safe and useful across diverse use cases

For security professionals, this research offers critical insights into how AI systems can maintain robust safety guardrails without compromising their core functionality—a key consideration as LLMs become more integrated into sensitive business applications.

Equilibrate RLHF: Towards Balancing Helpfulness-Safety Trade-off in Large Language Models