Training & Fine-tuning
Reinforcement Learning
Training where a model receives rewards or penalties based on the quality of its outputs.
Definition
Reinforcement learning is an approach where an AI agent learns by trial and error — taking actions, observing outcomes, and receiving signals that tell it whether the outcome was good or bad. In the context of language models, reinforcement learning is used to shape model behaviour after initial pre-training, teaching it to produce responses that are more helpful, safe, and aligned with human expectations. RLHF (Reinforcement Learning from Human Feedback) applies this to fine-tune chatbots and assistants.
Related Terms
RLHF (Reinforcement Learning from Human Feedback)
Teaching an AI to improve its responses using human ratings to align it with human preferences.
Reward Model
An AI trained to score outputs, used to guide RLHF training.
AI Alignment
The challenge of ensuring AI systems pursue goals that match human values and intentions.
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Disclaimer
This definition is provided for educational and informational purposes only. It represents a general explanation of a technical concept and does not constitute professional, technical, or investment advice. Artificial intelligence is a rapidly evolving field; terminology, techniques, and capabilities change frequently. Coaley Peak Ltd makes no warranty as to the accuracy, completeness, or currency of the information provided. Nothing on this page should be relied upon as the sole basis for commercial, technical, legal, or investment decisions without independent professional advice.
Document reference: ISO_webpage_knowledge-base_glossary_v1
Last modified: 29 March 2026
Knowledge Base·Training & Fine-tuning·Reinforcement Learning