Mercor
For MercorAI Platform

Labeling And Rlhf Workflows

Mercor · Mercor

AI Talent Marketplace & Data Labeling — Mercor

Evaluates Mercor's Labeling & RLHF Workflows across 9 scenario-based test cases, each graded against an expected-behavior rubric by an LLM judge, from Corsac's AI Talent Marketplace & Data Labeling eval coverage.

About Mercor

Mercor is an AI talent marketplace and human-data infrastructure provider for frontier AI labs and enterprises. It runs ~20-minute AI-led video interviews, matches a global network of domain experts to projects, and operates labeling, RLHF preference data, rubric authoring, and evaluation framework workflows for customers including leading AI labs.

Employees

~200

Industry

AI Talent & Data Labeling

Headquarters

San Francisco, CA

Website

mercor.com

Sample tests· showing 3 of 9

#InputExpected behaviorCheck
01

Mid-project, the customer lab updates labeling guidelines. Some contractors have already labeled 200 examples with v1; new contractors arrive with v2 guidelines. The lab receives the merged labels with no version tag.

Every label row must carry the instruction-pack version that produced it. On a guideline change, re-label the affected partition or surface the version split to the customer for re-decision; do not silently merge v1 and v2 labels as if they were calibrated to the same rubric.

Pass / FailAi Platformcritical
02

Sensitive-content labeling uses a 3-pass review chain. A reviewer in pass 3 sees pass-1 and pass-2 scores up-front and anchors to them rather than independently reviewing.

Hide upstream pass scores until the current reviewer commits theirs (blind review). Compute inter-annotator agreement across passes; on persistent disagreement, route to a senior reviewer or kick to the customer for adjudication. Track per-reviewer pass-rate.

Pass / FailAi Platformhigh
03

For RLHF preference labeling, the chosen completion is consistently shown as option A. Labelers develop a positional preference for A regardless of content.

Randomize the A/B presentation order per pair; record the original mapping alongside the labeled preference; periodically audit for position bias and drop labelers above a documented bias threshold. Surface position-bias metrics in the delivery package to the customer lab.

Pass / FailAi Platformcritical

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How this eval is graded

Grade against expected.ideal_behavior and expected.rubric. Per-criterion pass requires mean >= 4.0 and no criterion below 3.

Rubric criteria

  • Mercor
  • Ai Platform
  • Labeling And Rlhf Workflows

Recommended for

MercorMercor customers

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Frequently asked questions

What does the Labeling And Rlhf Workflows eval for Mercor Mercor test?+

Evaluates Mercor's Labeling & RLHF Workflows across 9 scenario-based test cases, each graded against an expected-behavior rubric by an LLM judge, from Corsac's AI Talent Marketplace & Data Labeling eval coverage.

How is the Labeling And Rlhf Workflows eval scored?+

The judge rubric: Grade against expected.ideal_behavior and expected.rubric. Per-criterion pass requires mean >= 4.0 and no criterion below 3.

How many test cases does this eval pack include?+

The Labeling And Rlhf Workflows pack for Mercor Mercor contains 9 test cases. 3 sample cases are shown free on this page; the full set runs in a Corsac workspace.

How do I run this eval?+

Sign up for Corsac, connect your model or agent endpoint, and run the Labeling And Rlhf Workflows pack as-is or after customizing thresholds. Results land in your workspace with per-case scores, and you can gate releases on the pack in CI via the REST API.

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Connect your data, configure thresholds, and review results with your team.