Scaling Expert Reasoning Data for Frontier Model Alignment

Client context

A frontier model lab preparing a domain-specialized release needed post-training data its internal team could not produce at volume: graduate-level reasoning traces and preference judgments in STEM and quantitative finance.

Challenge

Public data had been exhausted and generalist annotation vendors produced plausible-looking but technically wrong reasoning. The lab needed experts who could generate correct chains of thought — and a QA system that could prove correctness rather than assume it.

Data strategy

We scoped the model's target capabilities with the lab's research team, decomposed them into a task taxonomy, and built rubrics for both demonstration quality and preference judgment consistency. Production was structured as paired tasks: expert-written solutions plus independent rubric-based reviews.

Workflow

1. Scope — capability map and data gap analysis with the research team
2. Rubric — co-designed scoring rubrics, calibrated on 200 seed tasks
3. Expert production — screened SME pool producing CoT demonstrations
4. Review — independent dual review with disagreement resolution
5. Evaluation — weekly calibration against gold tasks and client spot checks

Quality controls

Every expert passed domain screening and calibration before production. Gold tasks were seeded at 8% of volume, inter-annotator agreement tracked per rubric dimension, and weekly drift reviews fed corrections back into the guidelines.

Outcome

The lab's internal evaluations showed measurable gains on held-out domain reasoning benchmarks after the first post-training cycle. Disagreement-resolution transcripts proved valuable enough that the client requested them as a standing deliverable — they now feed the lab's rubric development directly.

What changed after deployment

The engagement converted into a continuous data pipeline: evaluation findings from each model release define the next quarter's data specification.