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AI Glossary

Reinforcement Learning

A training technique where AI learns optimal behavior through trial, error, and reward signals.

TL;DR

  • —A training technique where AI learns optimal behavior through trial, error, and reward signals.
  • —Understanding Reinforcement Learning is critical for effective AI for companies.
  • —Remova helps companies implement this technology safely.

In Depth

Reinforcement Learning from Human Feedback (RLHF) is used to align LLMs with human preferences and safety requirements during training. Understanding RLHF helps explain why models behave as they do and why additional guardrails are needed for enterprise-specific policies.

Related Terms

AI Alignment

Ensuring AI systems behave according to human values, intentions, and organizational goals.

Foundation Model

A large AI model trained on broad data that can be adapted to many downstream tasks.

Fine-Tuning

The process of further training a pre-trained AI model on specific data to customize its behavior for particular tasks.

Training Data

The dataset used to train an AI model, which significantly influences its capabilities and biases.

Knowledge Hub

Glossary FAQs

Reinforcement Learning is a fundamental concept in the AI for companies landscape because it directly impacts how organizations manage a training technique where ai learns optimal behavior through trial, error, and reward signals.. Understanding this is crucial for maintaining AI security and compliance.
Remova's platform is built to natively manage and optimize Reinforcement Learning through our integrated governance layer, ensuring that your organization benefits from this technology while mitigating its inherent risks.
You can explore our full AI for companies glossary, which includes detailed definitions for related concepts like AI Alignment and Foundation Model.

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