Advanced Fine-Tuning
Overview
Advanced fine-tuning is the last step in building large language models (LLMs). It brings together everything the model has learned to make it more accurate and aligned with human understanding.
- Combines what was learned from earlier stages
- Uses feedback to correct and refine behavior
- Makes responses more useful and natural
This process ensures that models not only generate text but also understand how to respond better based on human expectations.
Reinforcement Learning with Human Feedback (RLHF)
Reinforcement Learning with Human Feedback (RLHF) is the third phase of training after pre-training and fine-tuning. It helps the model learn from human guidance.
- Uses human feedback to adjust responses
- Improves relevance, tone, and accuracy
- Learns continuously through ranked examples
As a recap:
- Pre-training teaches general language skills
- Fine-tuning focuses on specific tasks
Even after fine-tuning, models can produce incorrect or biased results because of noisy training data. RLHF helps fix that.
- Removes errors from general data
- Adds human validation to guide correct behavior
- Improves factual accuracy and tone
For example, a model trained on online data might mix facts with opinions. RLHF helps correct this by adding expert input.
How RLHF Works
RLHF improves the model using human feedback in three main steps.
- The model generates multiple possible responses
- A human expert ranks these responses
- The feedback trains the model to respond more like a person
Human reviewers help shape the model behavior by ranking the responses base on:
- Accuracy
- Relevance
- Coherence
Over time, this human-in-the-loop method helps the model develop more reliable and human-like communication.