Definition
Preference Learning is a methodology used to align LLMs and agents with specific human values or operational goals by training on ranked comparisons (e.g., Output A is better than Output B). In agentic systems, this refines the model's decision-making policy for tool-calling and reasoning, though it often requires a trade-off between strict alignment and the 'alignment tax' of reduced model creativity or generalization.
Distinguish from recommendation systems; in this context, it refers to fine-tuning model behavior (RLHF/DPO) rather than predicting consumer product choices.
"A Tasting Panel: A chef prepares two versions of the same dish, and instead of following a rigid recipe, they learn to cook based on which plate the judges consistently point to."
- RLHF (Reinforcement Learning from Human Feedback)(Prerequisite)
- DPO (Direct Preference Optimization)(Component)
- Reward Model(Component)
- Alignment Tax(Side Effect)
Conceptual Overview
Preference Learning is a methodology used to align LLMs and agents with specific human values or operational goals by training on ranked comparisons (e.g., Output A is better than Output B). In agentic systems, this refines the model's decision-making policy for tool-calling and reasoning, though it often requires a trade-off between strict alignment and the 'alignment tax' of reduced model creativity or generalization.
Disambiguation
Distinguish from recommendation systems; in this context, it refers to fine-tuning model behavior (RLHF/DPO) rather than predicting consumer product choices.
Visual Analog
A Tasting Panel: A chef prepares two versions of the same dish, and instead of following a rigid recipe, they learn to cook based on which plate the judges consistently point to.