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Service

RLHF & Preference Data

Corpshore AI produces RLHF and preference data for large language model alignment, ranked comparisons, preference pairs, SFT demonstrations, and reward-model data, with 4.2M preference pairs delivered for LLM alignment.

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4.2M
preference pairs delivered
97%+
accuracy via three-tier QA
35+
languages, native annotators
50-70%
cost advantage vs US-domestic
What we deliver

Scoped, staffed, and QA-gated

  • Preference pairs and ranked comparisons
  • SFT demonstrations and instruction data
  • Reward-model and evaluation datasets
  • Multilingual alignment data with native annotators
Preference rankingSFT demosReward modelingRed-team promptsRubric scoring
RLHF & Preference Data at Corpshore AI
Pain points we solve

The problems this service addresses

Reward-model gains stall because preference labels disagree with each other and no one can measure by how much.
Internal staff cannot sustain the weekly volume a training schedule needs, so runs slip waiting on data.
Non-English prompts get graded by second-language reviewers or machine translation, so multilingual preference signal is thin and unreliable.
Safety, reasoning, and coding slices share one generic rubric, blurring the distinctions the reward model needs to learn.
Rubric changes reach annotators unevenly, so early and late batches are labeled to different standards.
Marketplace throughput is unpredictable, so a lab cannot plan reward-model training against a known delivery date.
Case scenarios

Where teams use this work

Illustrative examples of how this service fits real programs. They are representative use cases, not named clients.

A frontier lab aligning a reasoning model

The lab needs ranked preference pairs across chain-of-thought reasoning, safety, and multilingual prompts at a weekly cadence its internal team cannot staff, with agreement measured on a shared gold set.

An enterprise assistant team

A team needs SFT demonstrations and preference data for domain-specific instructions, so the assistant learns which responses resolve a task rather than which merely sound fluent.

A multilingual product going global

A team needs alignment data judged by native speakers across target languages, so preference signal reflects how native users actually rank quality rather than a translated approximation.

A safety team hardening refusals

A team needs preference and red-team data on refusal behavior scored against a versioned safety rubric, so the reward model learns clean boundaries between safe and unsafe outputs.

How we deliver

From scope to delivery, end to end

Step through the stages of a rlhf & preference data engagement.

1. Map the prompt distribution

Define the reasoning, safety, coding, and multilingual slices with you, and set a target volume and cadence per slice before staffing.

Stage 1 of 5
Try it

A live look at the work

Switch tabs to see how a labeling, preference, or transcription unit moves through the QA cascade.

QA cascade active
carpedestriancyclist
What to expect

How the engagement runs

  • It starts by mapping your prompt distribution, reasoning, safety, coding, multilingual, and setting a target volume and cadence per slice before staffing.
  • A versioned rubric is published and calibration rounds run, so every annotator moves to each new standard together.
  • Embedded RLHF pods deliver a stable, plannable weekly cadence rather than unpredictable marketplace throughput.
  • Inter-annotator agreement is measured on a shared gold set and reported alongside each batch, so quality is tracked rather than assumed.
  • You provide the prompt distribution, rubric intent, and any gold examples; Corpshore provides the pods, calibration, QA, and cadence.
  • Delivery is preference pairs, demonstrations, or reward and evaluation data in your format, with agreement reported per hand-off.
Insights

What doing this well requires

  • Reward-model quality is capped by the consistency of human judgments, so calibration and agreement tracking do more for alignment than raw label volume.
  • Where annotators disagree, the fix is to clarify the rubric, not to average the disagreement away.
  • Each slice deserves its own calibrated rubric; reasoning, safety, and coding fail differently, and a shared rubric hides the distinctions.
  • Multilingual preference data is only as good as the reviewer's fluency, which is why native, in-region grading beats translated judgments.
  • A plannable weekly cadence is a product feature for a lab, because a known delivery date lets training runs be scheduled instead of stalled.
97%+
accuracy via QA cascade
35+
languages, native in-region
15,000+
seats across 18+ countries
50-70%
cost advantage vs US-domestic
Quality

Every unit passes a three-tier QA cascade

Tier 1

Annotator + peer review

Trained in-region annotators label to a versioned taxonomy. Every unit gets a structured peer check before it moves.

Catches ~80% of errors
Tier 2

Expert QA lead

Domain QA leads audit sampled and flagged work, resolve edge cases, and feed corrections back into annotator guidance.

Catches ~15% more
Tier 3

Programmatic + consensus

Automated consistency checks, gold-set benchmarking, and consensus scoring gate the batch before delivery.

Locks in 97%+ accuracy
FAQ

RLHF & Preference Data, answered

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.

Ready to scope a pilot?

Tell us your modality, volume, and languages. We'll return an indicative scope, timeline, and cost band.

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