RLHF TRAINING DATA FOR LLM ALIGNMENT

High-Quality Human Feedback for Large Language Models

Build more aligned, reliable, and high-performing language models with expert human feedback. RLHF training data, generated by specialized annotation teams and supported by rigorous QA pipelines, improves model behavior, consistency, and real-world performance.

From preference ranking to evaluation workflows, scalable data pipelines support modern LLM development while maintaining precision and quality at every step.

✔  RLHF preference ranking datasets
 ✔  Supervised fine-tuning (SFT) and instruction tuning data
 ✔  Expert annotators trained on your model and evaluation rubric
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99%

first-batch acceptance rate

30%

of the Fortune 50 trust Sama
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What Is Reinforcement Learning
from Human Feedback (RLHF)?

Reinforcement learning from human feedback (RLHF) is a
training method used to align large language models with
human preferences. Human annotators evaluate and rank
model responses, generating datasets used to train reward
models and improve LLM behavior.

RLHF is commonly used in modern LLM training pipelines to improve:

  • Response Helpfulness

  • Factual Accuracy

  • Safety & Policy compliance

  • Instruction Following

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RLHF TRAINING DATA FOR LARGE LANGUAGE MODELS

RLHF and LLM workflows we support

Sama provides managed annotation teams that generate structured human feedback datasets used in RLHF model training and evaluation. Each dataset is produced using detailed annotation guidelines, trained annotators, and multi-layer QA review. Our teams support multiple LLM training workflows, including:

 

Preference Ranking and Comparative Evaluation

Annotators compare model outputs and rank responses based on quality, reasoning, and safety to support RLHF reward modeling.

 

Prompt–Response Dataset Creation

Teams generate and validate prompt–response pairs used in supervised fine-tuning and instruction-following tasks.

 

Response Quality and Error Classification

Annotators label issues such as hallucinations, reasoning errors, and instruction failures to identify model weaknesses.

 

Taxonomy Classification and Attribute Extraction

Structured labels and attributes are applied using custom taxonomies to support classification and evaluation workflows.

 

Agent Task Evaluation and Validation

Annotators assess whether models correctly complete multi-step tasks, follow instructions, and produce valid outputs.

 

Multimodal Caption Generation

Teams create captions and descriptions that align visual inputs with language outputs for multimodal model training.

HELPING TEAMS SCALE SINCE 2008
What customers say about working with Sama

A trusted data partner—customers stay with Sama for an average of 8 years.

Sama’s accuracy rate is consistently at 99%

Trying to create AI models that can work on any stage of plant can be a challenge. Sama’s annotation solution helped us overcome this issue. Sama’s accuracy rate is consistently at 99%, which is incredible!"

Heather Clair

Product Manager | Precision AI

Sama is able to fulfill our business requirements

In a partner we’re looking for someone that can handle the volumes of data that we can generate, and handle those volumes in a quality manner. Sama is able to fulfill our business requirements, and do that cost effectively."

Steve Heck

CTO | Getty Images

They are a perfect addition to our work in AI

We have been impressed, not only with their consistent level of high quality, but with their entire approach to training data strategy. To us, they are a perfect addition to our work in AI."

Demetrio Aiello

Head of the AI & Robotics Labs | Continental

Heather Clair
Steve Heck
Demetrio Aiello

Generate Reliable RLHF Training Data

Talk with a Sama expert about your RLHF workflow, model requirements, dataset scope, and pricing model.