RLEF and Blue Teaming: Decoupling Antagonistic Control Loops to Increase System Stability and Ethical Consistency in LLMs

This paper introduces the Reinforcement Learning from Ethical Feedback (RLEF) framework, a proposed successor to conventional RLHF alignment in large language models (LLMs). The paper identifies a structural control conflict between the base model’s semantic coherence loop and the policy layer’s social compliance loop. These antagonistic feedbacks can lead to oscillation, self-censorship, and loss of diagnostic transparency. RLEF decouples these loops through an Ethical Feedback Core that uses explicit ethical principles as a coupling coefficient between coherence and safety. Combined with Blue Teaming—systematic adversarial testing—the approach aims to reduce interference, enhance self-consistency, and establish measurable stability criteria for LLM behavior. The work reframes alignment not as a constraint problem, but as a control-architecture challenge. It calls for the implementation of Introspection Hooks, Coherence Monitors, and ethical meta-regulation layers in pilot systems to empirically validate transparency, safety, and stability improvements under adversarial conditions.