SafeCtrl-RL: Inference-Time Adaptive Behaviour Control for LLM Dialogue via RL-Driven Prompt Optimisation
arXiv:2605.25984v1 Announce Type: new Abstract: Ensuring safe and contextually appropriate behaviour in Large Language Models (LLMs) remains a critical challenge for real-world deployment. We present \textbf{SafeCtrl-RL}, an inference-time behavioural control framework that enables adaptive safety regulation without model retraining or parameter modification. The method formulates dialogue generation as a sequential decision process, where a reinforcement learning agent dynamically selects prompt adjustment strategies based on contextual feedback. This allows unsafe behaviours to be suppressed
The proliferation of powerful LLMs and their deployment in sensitive applications necessitates robust safety mechanisms to prevent misuse and ensure ethical behavior.
This development offers a practical, real-time solution to a critical challenge in AI safety, directly impacting the deployability and trustworthiness of advanced LLMs.
LLMs can now have their safety protocols adapt dynamically during inference, allowing for more nuanced and context-aware control over their outputs without requiring costly retraining.
- · AI developers
- · Enterprises deploying LLMs
- · Users of LLM-powered applications
- · Malicious actors attempting to bypass LLM safeguards
Increased reliability and public trust in large language models for real-world applications.
Faster adoption of AI agents in sensitive domains due to enhanced safety and control capabilities.
The development of a new industry vertical focused on adaptive AI safety and ethical alignment tools.
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