Reinforcement Learning With Human Feedback - AI Training

It All Starts with Data Preprocessing

The story starts here: good data inputs make for good model outputs. Our Advanced AI Data Trainers do what tech can’t with thoughtful data preparation that supports users building machine learning models on the Amazon SageMaker platform. We have the capacity to deploy hundreds of intelligent operators in months, and preprocess data that makes your model strong from the get go.

Next Step: Human-in-the-loop AI Training

A human-in-the-loop approach makes AI models better at most tasks. Our operators align with your quality benchmarks for your reinforcement learning framework and evolve with it as datasets continue to improve your model. Normally the fun stops here because this process scales badly. But most vendors don’t have the agility or recruiting infrastructure that Invisible does.

RLHF Ensures Models Get Better With Age

Work doesn’t stop when a model is deployed. On top of your fine-tuned model’s ability to continuously improve, we improve with it and maintain a steady beat of reinforcement to make your model smarter over time. For one client, our skilled AI data trainers are providing 3,000+ hours of high-quality RLHF every day.

What is Reinforcement Learning with Human Feedback?

Reinforcement Learning with Human Feedback (RLHF) is a subfield of Reinforcement Learning (RL) that involves incorporating feedback from human evaluators and a reward system to improve the learning process.

The problem: It’s really hard to scale.

To get the most out of RLHF trained models, you need a lot of skilled data trainers to prepare data and give the model intelligent & consistent feedback. Invisible offers one of the only cost-effective solutions in the market.

Here's an example of where we created value for a major AI platform: Invisible overcame machine limitations when other contractors couldn’t by recruiting over 200 skilled operators in 3 months, completing over 5,000 comparison tasks each week, and beating quality benchmarks by 10%.