Code RLHF Data,
Written and Reviewed
by Senior Engineers.
The hardest part of training coding models isn't compute, but getting data that reflects how senior engineers think rather than surface-level judgments from junior annotators. Beyond Labs provides code RLHF data: code reviews, and reasoning from engineers with 6+ years of production experience. Each batch is built around task specs, gold-set sampled, peer-reviewed, and signed off by a senior calibrator-no crowdwork or AI-first drafts.
Task Types We Deliver
Code Generation Prompts & Reference Solutions
Multi-file, multi-step, with edge-case handling. Written to spec - not pulled from Stack Overflow.
Bug-Fix Demonstrations
Original buggy code, root-cause analysis, fix, and regression tests - structured as a training-ready trajectory.
Code Review Annotations
Line-by-line feedback on AI-generated or human-generated code, scored against your rubric by senior engineers.
Refactoring Trajectories
Step-by-step transitions from messy, untested code to production-ready output with annotated rationale at each step.
Reasoning Verbalizations
First-person reasoning logs that capture how an engineer thinks through a problem - not just what they ultimately type.
Code Preference Comparisons
Pairwise comparisons with annotated rationale, suitable for DPO and reward-model training pipelines.
Languages and Stacks
Native Coverage
Frameworks across web, mobile, ML, data, and systems work. Specialist coverage available for less common stacks on request.
Quality Controls
Gold-set sampling
A blind 5-10% of every batch is graded against a reference, calibrated jointly with the client.
Peer review
Every output is reviewed by a second senior engineer before it leaves our system.
Senior calibrator sign-off
A lead engineer per project signs off batches, tracks drift, and runs weekly calibration sessions with the client.
Drift detection
Inter-reviewer agreement tracked across the batch lifecycle, with re-calibration if any metric slips.
How We Price
Single block
Pricing scales with task complexity and required seniority, not a flat per-token rate. We publish indicative tiers on our pricing page. Most engagements are structured as per-task with a defined throughput SLA, plus a setup fee for calibration.
