SafeCtrl-RL: Inference-Time Adaptive Behaviour Control for LLM Dialogue via RL-Driven Prompt Optimisation

📰 ArXiv cs.AI

Learn how to control LLM dialogue safety using reinforcement learning-driven prompt optimization with SafeCtrl-RL

advanced Published 26 May 2026

Action Steps

Formulate dialogue generation as a sequential decision process using SafeCtrl-RL
Implement a reinforcement learning agent to dynamically select prompts for safe dialogue
Configure the RL agent to optimize prompts based on safety and contextual appropriateness
Test the SafeCtrl-RL framework on various LLM dialogue tasks to evaluate its effectiveness
Apply SafeCtrl-RL to real-world LLM deployments to ensure adaptive safety regulation

Who Needs to Know This

NLP engineers and researchers can benefit from this framework to ensure safe and contextually appropriate LLM behaviour in real-world applications

Key Insight

💡 Reinforcement learning can be used to optimize prompts for safe and contextually appropriate LLM dialogue without requiring model retraining

Key Takeaways

Learn how to control LLM dialogue safety using reinforcement learning-driven prompt optimization with SafeCtrl-RL

Full Article

Title: SafeCtrl-RL: Inference-Time Adaptive Behaviour Control for LLM Dialogue via RL-Driven Prompt Optimisation

Abstract:
arXiv:2605.25984v1 Announce Type: cross 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 pro

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