RLHF Tuning
Human preference data, reward-model training sets, and fine-tuning pipelines — curated by domain experts to align your models with real-world intent.
Aligning Models With Human Intent
RLHF tuning is only as good as the human signal behind it. At CoreLabel, we build the preference datasets, ranking corpora, and feedback pipelines that make reward models trustworthy.
Our annotators are trained on nuanced preference elicitation — capturing helpfulness, harmlessness, honesty, and domain accuracy across single-turn and multi-turn interactions. Every dataset is versioned, auditable, and delivered with inter-annotator agreement metrics.
Preference Ranking
Pairwise and listwise human preference annotations for reward model training. Annotators evaluate response pairs across multiple quality axes — helpfulness, correctness, safety, and tone.
Comparative Judgements
Expert-evaluated response comparisons across helpfulness, safety, and accuracy dimensions. Structured rubrics ensure consistency across annotators and batches.
Safety & Alignment Data
Red-teaming outputs, refusal data, and constitutional AI feedback collections. Purpose-built to surface failure modes and train robust refusal behaviours.
Supervised Fine-Tuning (SFT) Datasets
High-quality instruction-following, chain-of-thought, and dialogue datasets tailored to your model's domain and intended behaviour profile.
Reward Model Training Data
Curated scored responses and ranked completions that give your reward model a reliable signal — including hard negatives and edge-case examples.
Iterative Feedback Loops
We integrate with your training pipeline to collect live model output evaluations, enabling continuous improvement cycles as your model evolves.
The RLHF Tuning Pipeline
Common Use Cases
Chatbot & Assistant Alignment
Align conversational models on tone, helpfulness, and refusal behaviour for consumer and enterprise deployments.
Healthcare & Legal NLP
Specialised annotators with domain expertise for sensitive, high-stakes preference data.
Code Generation Models
Expert developer annotators evaluate correctness, efficiency, and style across code completion outputs.
Multilingual RLHF
Native-speaker annotators across 20+ languages for culturally aligned preference datasets.
Ready to Tune a Better Model?
Tell us about your model, use case, and scale — we'll design an RLHF tuning pipeline that delivers reliable human signal from day one.