How Training Actually Works
Continuing to train a base model on your own examples to specialize its behavior. This guide pulls together everything on Onexial tagged training — 5 connected nodes across definitions, workflows, tool stacks, comparisons, prompts and applied use cases — and orders it the way you would actually learn it: vocabulary first, then process, then tooling, then execution. Every item below links to a full node with its own examples and connections, so you can go as deep as you need without losing the map.
Core concepts behind Training
Before wiring anything together, the vocabulary has to be precise. These 5 definitions cover the terms that show up in almost every Training discussion — each one links to a full entry with an example and its own connections inside the graph.
Fine-Tuning
Continuing to train a base model on your own examples to specialize its behavior.
RLHF
Reinforcement learning from human feedback, the alignment technique behind ChatGPT.
DPO (Direct Preference Optimization)
A simpler alternative to RLHF that skips the reward model.
Model Distillation
Training a small model to mimic a large one.
LoRA
Low-Rank Adaptation: cheap fine-tuning that patches only small matrices.