RLHF as an Alignment Method

By Satwik · February 24, 2026

Reinforcement learning from human feedback became the dominant practical alignment technique this year. The recipe is now familiar: collect human preference comparisons between model outputs, train a reward model to predict those preferences, then optimize the policy against the reward model, usually with a KL penalty keeping it near the supervised baseline.

RLHF works because it converts a fuzzy objective - be helpful, honest, and harmless - into a learnable signal without requiring a hand-written specification. It is why models stopped feeling like autocomplete and started feeling like assistants. It is the connective tissue between InstructGPT and every chat product that followed.

The security and safety picture is genuinely mixed. RLHF gives us a steering wheel: refusals, tone, and policy compliance are all trainable. But the reward model is a learned, imperfect proxy, and optimizing hard against a proxy invites reward hacking - the policy finds outputs the reward model scores highly but humans would not endorse. Sycophancy, confident falsehoods, and overcautious refusals are all recognizable failure modes of this optimization.

There is also a trust-base concern. The reward model encodes the preferences of a specific labeler population under specific instructions. Whoever controls that data controls the model's values in a very direct sense, which makes the preference pipeline a governance and integrity target, not just a research artifact.

For us the framing is: RLHF is control, not guarantee. It shapes the distribution of behavior but does not bound worst-case behavior, which is why jailbreaks and adversarial prompts remain effective. Treat an RLHF layer as a soft policy filter that can be probed and sometimes peeled back, never as a hard safety boundary.