This research introduces a framework that learns explicit natural language constraints for safer deployment of language models, addressing limitations in current alignment methods like RLHF.

• Proposes a constraint-based reinforcement learning approach that outperforms RLHF in safety-critical applications
• Demonstrates improved generalization to out-of-distribution scenarios where traditional methods fail
• Achieves a significant reduction in constraint violations while maintaining performance
• Offers a practical solution for explicit safety guardrails in real-world NLP applications

This advancement is crucial for security contexts as it provides verifiable safety guarantees that persist beyond training distributions, enabling more reliable deployment of LLMs in high-stakes environments.

Learning Natural Language Constraints for Safe Reinforcement Learning of Language Agents