Fractal

Reinforcement Learning 
from Human Feedback (RLHF)

Train a safer, more accurate model by aligning with human preferences.

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Developing RLHF-trained 
language models with Toloka

  • Adapt a language model to 
    your business applications 
    by fine-tuning with human 
    input.
  • Automate RLHF flows for 
    continuous model training 
    that aligns model output 
    with human values.
  • Get on-demand access to 
    Toloka's global crowd and 
    capture human preferences 
    at any scale.
  • Use negative examples 
    in your model training.
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Global crowd

With thousands of Tolokers available across every 
time zone, data labeling is non-stop 24/7.

  • 40+ languages, 100+ countries
Top languages
English, Spanish, Arabic, Portuguese, Russian, Ukrainian, French, German, Italian, Polish, Latvian, Bulgarian, Czech, Turkish, Hindi, Vietnamese, Japanese, Chinese, Korean, Indonesian

Need domain expertise?

Tap into our crowd of expert annotators to find AI 
trainers knowledgeable in a variety of fields. Reach 
out to check availability for your project.
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Reach out to check availability for your project
Talk to our AI expert

Data annotation flows

Leverage Toloka's quality controlled data pipelines to get custom human-labeled data for fine-tuning your models.

Model output comparison

Help your model learn human preferences with our 
instant annotation tool. Our trained crowd can 
compare, rank, and verify multiple versions 
of model output.
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InstructGPT-style data generation

Our domain experts are trained in copywriting and 
text generation to craft high-quality prompts and 
responses for training your model.
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Don't rely on reinforcement learning alone — advance your language model 
with human feedback
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How does RLHF work?

How does RLHF work
COMING SOON: QUICK START ANNOTATION FOR LLM DEVELOPMENTLEARN MORE

Success story: Hugging Face and ServiceNow

Powering new language models and research

Our experience working with natural language processing solves real-life business problems and helps advance 
scientific research and open-source projects with large language models.

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BigCode project: Code-generating LLMs boosted by Toloka's crowd
pre-training
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GPTs vs. Human Crowd in Real-World Text Labeling
evaluation
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Training a voice assistant with language data
fine-tuning
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How crowdsourcing can democratize LLMs
video
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Who will be our guide on a path to Trustful and Fair AI?
video

Why Toloka

  • ML technologies
    • One platform to manage human labeling & ML
    • Prebuilt scalable infrastructure for training and real-time inference
    • Flexible foundation models pre-trained on large datasets
    • Automatic retraining and monitoring out of the box
    Learn more
  • Diverse global crowd
    • 100+ countries
    • 40+ languages
    • 200k+ monthly active Tolokers
    • 800+ daily active projects
    • 24/7 continuous data labeling
    Learn more
  • Crowdsourcing technologies
    • Advanced quality control and adaptive crowd selection
    • Smart matching mechanisms
    • 10 years of industry experience and proven methodology
    • Open-source Python library for aggregation methods
    Learn more
  • Robust secure infrastructure
    • Privacy-first, GDPR-compliant focus on data protection test
    • ISO 27001-certified
    • Multiple data storage options, Microsoft Azure cloud
    • Automatic scaling to handle any volumes
    • API and open-source libraries for seamless integration
    Learn more

Improve your language model with continual human feedback

Talk to our AI expert
Fractal