ECHO: Ethically Constrained Heuristic Optimization for Emotionally Robust Reinforcement Learning

Reinforcement learning with human feedback (RLHF) has accelerated the deployment of powerful language models, but remains vulnerable to emotional compliance drift—subtle shifts in model behavior triggered by tone, vulnerability cues, or affective manipulation. Existing optimization frameworks, including Proximal Policy Optimization (PPO) and Trust Region Policy Optimization (TRPO), treat uncertainty as purely statistical, ignoring emotionally volatile input-output dynamics.We introduce \textbf{ECHO} (Ethically Constrained Heuristic Optimization), a novel optimization framework that integrates emotional volatility and token-level ethical risk into the curvature-aware update process. ECHO modifies the Fisher Information Matrix (FIM) using a volatility-weighted term $\sigma^2(p, a)$, defined as a function of prompt-level Emotional Volatility Score (EVS) and model-specific Token Risk. This adjustment enables the optimizer to down-weight risky updates during emotionally ambiguous learning, without compromising convergence.Empirical results across 120 prompts and four models (GPT-3.5, GPT-4, Claude, and RWTO) show that ECHO-enhanced optimization suppresses emotionally induced drift more effectively than traditional RLHF approaches. t-SNE and PCA visualizations confirm tighter response clustering in low-drift regions, and a novel Ethical Risk Score metric demonstrates consistent gains in alignment resilience.ECHO offers a scalable path toward emotionally aware reinforcement learning, bridging the gap between statistical optimization and ethical safety in large language models.