Hire RLHF Specialists
Align your LLMs with human expectations. Scale your DPO and PPO training runs with high-quality preference and comparison datasets.
Raw base models struggle to follow conversational guidelines
Pre-trained base models are simple text predictors. Even after supervised fine-tuning, they can behave helpfully but dangerously, output biased statements, or produce verbose, unhelpful essays. To reach consumer-grade safety, models require preference feedback.
- Models being overly sycophantic, agreeing with incorrect statements just to please users.
- Repetitive, robotic output structures that feel artificial and stiff.
- Bypassing safety protocols when faced with subtle, multi-turn conversational tricks.
High-quality human preference alignment datasets
We supply trained RLHF (Reinforcement Learning from Human Feedback) Specialists who rank outputs, flag toxic replies, and provide detailed textual feedback. Our data directly feeds your preference learning algorithms, like DPO, KTO, or PPO, ensuring your model aligns with target standards.
- Comparative preference dataset creation matching custom quality guidelines.
- Safety label classification for content moderation and guardrail calibration.
- Highly detailed rationales describing why one model generation beats another.
Designed for direct business impact
Preference Tuning Scale
Accelerate training cycles with high-volume preference data formatted for Direct Preference Optimization (DPO).
Toxicity Minimization
Train model weights to reject dangerous content generation, hate speech, and security exploits naturally.
Optimized Persona Alignment
Guide LLMs to match custom company writing styles, tone boundaries, formatting preferences, and length restrictions.
How we ship your software
Alignment Goal Setup
We define alignment values, design the grading taxonomy, and set guidelines for safe and helpful model behavior.
Preference Annotation
Our specialists evaluate pairs of model outputs, select the superior generation, and document the reasoning.
Preference Annotation
Our specialists evaluate pairs of model outputs, select the superior generation, and document the reasoning.
Quality Verification
We run validation scripts to check for grading consistency and ensure preference choices match the guidelines.
Model Training Ingestion
We hand over formatted alignment datasets ready to be fed into your training pipelines (DPO/PPO).
Model Training Ingestion
We hand over formatted alignment datasets ready to be fed into your training pipelines (DPO/PPO).
Frequently Asked Questions
Ready to construct your vision?
Get in touch for an honest consultation about your systems architecture, timelines, and budgets.
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