What This Research Area Covers
Alignment research studies how to ensure an AI system's behavior actually reflects the goals and values its developers and users intend, addressing the gap between what a model is trained to optimize for and what humans genuinely want.
Why It Matters
A highly capable model that doesn't reliably do what's intended is a significant problem regardless of raw capability — alignment research aims to close that gap as models become more powerful and are given more autonomy.
Current Research Directions
Techniques like RLHF and Constitutional AI for shaping model behavior, interpretability research to understand a model's internal reasoning, and studying how alignment techniques hold up as models scale in capability are all active areas.
Related Pages
Frequently Asked
Is alignment the same as safety?
Closely related and often used together; alignment specifically focuses on the gap between intended goals and actual model behavior, while safety is a broader umbrella term.
What is Constitutional AI?
Anthropic's published technique for aligning model behavior using a set of guiding principles rather than relying solely on human feedback — see our Anthropic research page.
Does alignment get harder as models get more capable?
This is a genuinely debated open question in the field, with active research on whether current techniques will continue to work reliably at greater scale.
Where can I learn more about RLHF specifically?
See our Reinforcement Learning topic page.