Why Alignment Needs More Data Quality
Compute keeps scaling. The next bottleneck is the human signal that teaches models what good looks like.
Frontier labs keep scaling parameters and compute. The harder problem is the data that teaches models human preferences.
Most alignment datasets were built quickly to ship a release. They carry annotator fatigue, fuzzy guidelines, and shortcut labels.
The cost of noisy preferences
Reward models trained on noisy data converge on confidently wrong rankings. The policy then optimizes against a flawed proxy.
We see this in production as confident hallucinations, sycophancy, and refusal-on-the-wrong-things. The training signal pushed the model there.
What better data looks like
Calibrated rubrics, redundant labels, and per-domain annotator pools are the basics. Active learning loops surface cases where annotators disagree.
The labs that invest in this layer will pull ahead. Compute is no longer a moat — clean preference data still is.
