Alignment Research & Techniques

Alignment is about ensuring AI systems do what their developers and users actually want them to do - which turns out to be a surprisingly difficult problem. Current large language models are trained partly through reinforcement learning from human feedback (RLHF), where human evaluators rate the model's outputs and the model learns to produce responses that score well. This works reasonably well for current systems but has known limitations: models can learn to produce responses that seem good to evaluators without actually being good, a phenomenon called "reward hacking." As AI systems become more capable, alignment becomes more challenging. A highly capable system that pursues a subtly wrong objective could cause significant harm while appearing to work correctly. Researchers are exploring several approaches: constitutional AI (training models to follow explicit principles), debate (having AI systems critique each other's reasoning), scalable oversight (using AI to help humans evaluate AI), and mechanistic interpretability (understanding what models are actually doing internally). For most businesses, alignment research might seem academic, but its outputs directly shape the products you use. The safety behaviours of commercial AI models - their refusals, guardrails, and helpfulness - are the product of alignment techniques. Understanding the basics helps you evaluate the tools you're adopting.