A Practical Guide to Frontier Alignment Data
What alignment data actually is — SFT, RLHF, DPO, red teaming — how the formats differ, and how to specify quality so expert data improves your model instead of polluting it.
By Data Team
Frontier alignment data is expert-produced training and evaluation data used to align large model behavior with human intent during post-training. It spans supervised fine-tuning demonstrations, preference data for RLHF and DPO, reasoning traces, and adversarial red-teaming prompts.
The four formats that matter
SFT demonstrations
Prompt–response pairs where the response is exactly what you want the model to produce. Quality requirement: the response must be correct, not merely fluent — which is why generalist annotation pools fail on technical domains.
Preference data (RLHF / DPO)
Pairs or rankings of candidate responses judged against a rubric. The rubric is the product: ambiguous rubrics produce noisy preferences, and noisy preferences produce reward hacking.
Reasoning traces
Step-by-step chains of thought written or verified by domain experts. These are the highest-leverage and highest-cost format — a single wrong step teaches the model to be confidently wrong.
Adversarial / red-team data
Prompts designed to elicit failures, paired with annotations of what failed and why. Useful both for safety training and for building private evaluation sets.
How to specify quality
| Dimension | Weak spec | Strong spec |
|---|---|---|
| Expertise | "experienced annotators" | named domains, screening pass rates, calibration scores |
| Agreement | unmeasured | per-dimension inter-annotator agreement targets |
| Gold tasks | none | seeded %, refresh cadence, drift thresholds |
| Review | single pass | independent dual review + disagreement resolution |
The mistake most teams make
Buying volume before calibrating. Run a small pilot, measure agreement against your own researchers' judgments, fix the rubric, then scale. Data produced before calibration is usually a write-off.
Where this fits in the data engine
Alignment data is one cycle of a loop: evaluation findings define the next data specification. If your vendor cannot tell you which failure modes a batch is meant to close, you are buying data, not progress.