Multi-Objective Language Model Alignment

This field studies how to align language models with multiple goals that can conflict, including helpfulness and harmlessness, accuracy and fairness, or personalization and consistency. Common approaches combine reward models with reinforcement learning or direct preference optimization, while others use Pareto-front search, adaptive objective weights, gradient balancing, or preference-conditioned policies to preserve different trade-offs. Researchers also explore model merging and inference-time steering to let users adjust values without retraining, and build evaluations for settings such as medical advice, dialogue, and tool use. A central challenge is that objectives can interfere and human preferences are diverse or inconsistent, so methods must balance them without sacrificing capabilities or hiding important trade-offs in a single score.