Find the term.
See how it fits.
Explore the concepts behind AI agents, one useful connection at a time.
Concept directory
Fine-tuning
Fine-tuning continues training from an existing model to adapt it to data, a task, or a domain. It changes trainable parameters, rather than only changing the input supplied for one request.
Reinforcement Learning
Reinforcement learning trains an agent’s behavior using reward signals from interaction. The objective concerns expected cumulative reward, so an action’s value can depend on consequences that occur later.
Reward
A reward is a feedback signal used to guide learning or assess outcomes against an objective. In language-model training, a learned reward model can assign scores based on preference examples.
Rollout
In reinforcement learning, a rollout is a sampled sequence of interaction: observations or states, actions, and resulting rewards. It provides experience for analysis or learning.
Supervised Fine-tuning
Supervised fine-tuning adapts a pretrained model using examples of desired behavior. For language models, training can teach the model to produce target responses or continue supplied text.