Training: The Complete Guide
Continuing to train a base model on your own examples to specialize its behavior. Fine-Tuning adjusts a pretrained model's weights using a curated dataset so it follows a specific style, format, or task more reliably than prompting alone. 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.
Frequently asked questions
- What is Fine-Tuning?
- Fine-Tuning adjusts a pretrained model's weights using a curated dataset so it follows a specific style, format, or task more reliably than prompting alone. It is most valuable when you need consistent tone, strict output schemas, or to compress a long system prompt.
- What is an example of Fine-Tuning?
- An e-commerce brand fine-tunes a small model on 5,000 of their best support replies, then runs it for first-line triage — keeping voice and policy consistent without a 3,000-token system prompt.
- Why does Fine-Tuning matter for AI and automation?
- Continuing to train a base model on your own examples to specialize its behavior. It connects to the workflows, prompts and tool stacks linked on this page, so you can move from definition to execution without leaving Onexial.
- What is RLHF?
- RLHF trains a reward model from human preference pairs, then fine-tunes the base LLM with PPO to maximize that reward. Result: models that follow instructions and refuse unsafe requests.
- What is an example of RLHF?
- ChatGPT's instruction-following polish comes from RLHF applied on top of GPT-3.5.
- Why does RLHF matter for AI and automation?
- Reinforcement learning from human feedback, the alignment technique behind ChatGPT. It connects to the workflows, prompts and tool stacks linked on this page, so you can move from definition to execution without leaving Onexial.
- What is DPO (Direct Preference Optimization)?
- DPO fine-tunes directly on preference pairs (chosen vs rejected) with a contrastive loss. Cheaper and more stable than PPO; the default for open-source alignment.
- What is an example of DPO (Direct Preference Optimization)?
- Most fine-tunes of Llama-3 for chat use DPO on 10k-100k preference pairs.