Model Alignment

Model alignment is the overarching field of AI research dedicated to ensuring that artificial intelligence systems act in accordance with human intentions, values, and ethical principles. It is the process of taking a 'raw' AI—which is highly capable but fundamentally amoral and unpredictable—and constraining it so that it is helpful, honest, and harmless.

The alignment problem stems from the fact that LLMs are trained to predict the next word based on internet data, which is full of toxicity, falsehoods, and bias. An unaligned model is just as likely to help a user write a benign python script as it is to help a user synthesize a dangerous chemical. Alignment techniques, primarily Supervised Fine-Tuning (SFT) and Reinforcement Learning from Human Feedback (RLHF), are used to suppress undesirable behaviors and promote safe, useful ones.

Alignment is a deeply complex sociotechnical challenge. The core philosophical question is: *Whose values* are we aligning the model to? Different cultures, governments, and individuals have vastly different definitions of what constitutes 'harmless' or 'helpful' speech. Furthermore, there is often an 'alignment tax'—the observation that heavily aligning a model for safety often slightly degrades its raw coding or reasoning capabilities, forcing AI labs to constantly balance safety against utility.