Model Alignment

Model alignment addresses a fundamental challenge: a model trained to predict the next token is not automatically trained to be helpful, honest, or safe. Without deliberate alignment work, large language models can generate confident misinformation, produce biased outputs, follow harmful instructions, or pursue proxy objectives that diverge from user intent. Alignment closes this gap between "what the model is capable of" and "what the model should do."

The predominant technique for alignment is Reinforcement Learning from Human Feedback (RLHF), where human raters score model responses and a reward model is trained on those preferences — the base LLM then fine-tunes against the reward signal. More recent approaches include Direct Preference Optimization (DPO), which achieves similar results without a separate reward model, and Constitutional AI (Anthropic's method), where the model is trained to critique and revise its own outputs against a set of written principles. For enterprise deployments, alignment also extends to domain-specific fine-tuning: training models to follow company policies, refuse certain request categories, and stay within defined scope.

The business impact of misalignment can be severe — from reputational damage caused by offensive outputs, to regulatory violations from biased hiring or lending decisions, to security breaches enabled by a model that can be manipulated into ignoring its instructions. Enterprises should evaluate foundation model providers on their alignment methodology, invest in red teaming before deployment, and layer runtime guardrails on top of model-level alignment for defense in depth.