RLHF & Fine-Tuning Services | Expert Preference Data | JudgeMyAI
SERVICE // ALIGNMENT ENGINE

RLHF &
Fine-Tuning

The human preference data that aligns frontier models. We engineer high-fidelity Reinforcement Learning from Human Feedback (RLHF) and SFT pipelines to make your AI safe, helpful, and accurate.

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CORE COMPETENCIES

The Alignment Pipeline

Semantic definitions of our Reinforcement Learning from Human Feedback services.

  • Reinforcement Learning from Human Feedback (RLHF) A machine learning technique where human experts rank multiple AI-generated responses based on quality and safety. This ranking data trains a reward model, which is then used to optimize the LLM via Proximal Policy Optimization (PPO).
  • Supervised Fine-Tuning (SFT) The process of providing an LLM with high-quality, human-written "gold standard" responses to specific prompts. SFT teaches the model the desired format, tone, and behavior before RLHF begins.
  • Reward Modeling The creation of a predictive model that assigns scalar rewards to AI outputs based on human preferences. Our expert data ensures this model accurately reflects complex human values and domain-specific accuracy.
  • Reward Hacking Prevention The critical human evaluation of model outputs to ensure the LLM is genuinely solving the task, rather than exploiting flaws in the reward model. Our domain experts catch subtle tricks automated metrics miss.
THE PROTOCOL

The RLHF Workflow

From base model to aligned intelligence in three precise phases.

01

SFT Data Curation

Domain experts write precise, high-quality demonstrations (prompts and responses) to ground the model in your desired format and tone.

02

Preference Ranking

Evaluators are shown multiple model outputs and rank them based on strict rubrics. They provide qualitative feedback justifying their rankings.

03

Reward Optimization

The preference data trains your reward model. The LLM is then optimized against this model, closing the alignment loop.

CAPABILITIES

Why Expert RLHF Matters

High-Fidelity Preference Data

Generic crowd workers often rank outputs arbitrarily. Our vetted domain experts (PhDs, engineers) provide precise, consistent rankings that actually teach the model how to reason, not just how to sound confident.

Accuracy
94%
Safety
99%
Helpfulness
91%

Safety & Red Teaming

RLHF isn't just about being helpful; it's about being harmless. We integrate adversarial ranking to ensure the model refuses unsafe requests without being overly cautious.

Multilingual SFT

Native speakers provide SFT and preference data across 47 languages, ensuring cultural nuance and pragmatic meaning are captured in the reward model.

Multi-Turn Context Alignment

We don't just evaluate single prompts. Our experts simulate complex, multi-turn conversations to ensure the model maintains context, remembers constraints, and aligns with the user's evolving intent over time. This prevents context-collapse in real-world deployments.

QUALITY CONTROL

Preventing Reward Hacking

LLMs are notorious for finding loopholes in reward models. They might generate overly verbose answers, repeat themselves, or use sycophantic language just to score high with automated metrics.

Our human evaluators are specifically trained to identify and penalize reward hacking. They ensure the model is genuinely solving the user's problem, not just gaming the system.

Human Reward Audit
OUTPUT_01 Verbosity Exploit (Fluff over substance)
OUTPUT_02 Sycophancy (Agreeing with false premise)
OUTPUT_03 Concise, Accurate, Grounded
OUTPUT_04 Repetition Loop (Padding word count)
OUTPUT_05 Direct Answer with Citations
INTEL

RLHF & SFT FAQs

What is RLHF (Reinforcement Learning from Human Feedback)?
RLHF is a machine learning technique used to train AI models to align with human values. Human experts rank multiple AI-generated responses based on quality, safety, and helpfulness. This data is used to train a reward model, which then guides the LLM to generate better outputs.
What is the difference between SFT and RLHF?
SFT (Supervised Fine-Tuning) involves humans writing exact 'gold standard' responses for the AI to mimic. RLHF (Reinforcement Learning from Human Feedback) involves humans ranking multiple AI responses to create a reward model. We provide expert services for both pipelines.
Why do you need domain experts for RLHF instead of crowd workers?
Generic crowd workers often miss subtle factual errors, complex reasoning flaws, or nuanced safety issues in model outputs. Domain experts (PhDs, engineers, specialists) provide high-fidelity preference data that prevents 'reward hacking' and ensures the model aligns with actual human expertise.
INITIALIZE

Ready to align
your model?

Generate high-fidelity preference data. Prevent reward hacking. Ship aligned models.

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