Question 1

What RLHF data can you produce?

Accepted Answer

Ranked preference pairs, SFT demonstrations and instruction data, reward-model and evaluation datasets, and rubric-based scoring. This includes multilingual alignment data produced by native, in-region annotators, so preference signal in non-English languages reflects how native speakers actually judge quality.

Question 2

Do you measure inter-annotator agreement?

Accepted Answer

Yes. Calibration rounds align annotators to a versioned rubric before production, and consensus scoring tracks agreement throughout, producing measured, high-agreement data your reward models can rely on. Where agreement drops, the rubric is clarified rather than the disagreement being averaged away.

Question 3

Can you scale weekly cadence?

Accepted Answer

Embedded RLHF pods deliver a stable, plannable weekly cadence rather than unpredictable marketplace throughput. For one frontier lab, Corpshore delivered 4.2M preference pairs across reasoning, safety, and multilingual prompts at a cadence the lab could plan its training runs around.

Question 4

What is RLHF data and why does reward-model quality depend on it?

Accepted Answer

RLHF, reinforcement learning from human feedback, uses human judgments about which model outputs are better to train a reward model that then guides the base model. Reward-model quality is capped by the consistency of those judgments, so inconsistent or unmeasured labeling directly limits how well alignment can work, which is why calibration and agreement tracking matter.

Question 5

Can you handle safety, reasoning, and domain-specific preference data?

Accepted Answer

Yes. Pods are built around your prompt distribution, whether that is chain-of-thought reasoning, safety and refusal behavior, coding, or a specialized domain. Annotators are trained on the rubric for that slice, and difficult categories get calibration rounds before they enter production.

Question 6

Do you provide multilingual alignment data?

Accepted Answer

Yes, across 35+ languages with native, in-region annotators. Preference and evaluation data in a language is judged by people who live in that language, which avoids the failure mode where non-English alignment is graded by second-language speakers or machine translation.

Question 7

How do you keep the rubric consistent as it evolves?

Accepted Answer

The rubric is versioned, and changes are rolled out through calibration rounds so every annotator moves to the new standard together. Older batches can be re-scored against the current rubric when a program's definition of quality shifts, keeping the dataset internally consistent.

RLHF & Preference Data

Scoped, staffed, and QA-gated

Every unit passes a three-tier QA cascade

Annotator + peer review

Expert QA lead

Programmatic + consensus

RLHF & Preference Data, answered

Ready to scope a pilot?