Pre-training, Finetuning, Post-training
Training is deeply associated with the process of adjusting the model weights. While prompt engineering influences the output by modifying the the given input/context, prompt engineering doesn’t change the model weights.
Pre-training, training a model from scratch. Model weights are initialised. Then huge amount of training data is processed to adjust the model weights. This is the most resource-intensive phase.
Finetuning, continuation of the training with the weights obtained from the previous training sessions. This process usually uses much smaller or specialised dataset.
Post-training, from one perspective, the finetuning and the post-training are the same as both happen after the model is pre-trained as the mission of both is to improve the model.
Finetuning vs Post-training
Then, what’s the difference between finetuning and post-training?
I am not sure if this widely accepted definition, but in the context of foundation models, according to the book, finetuning is made by users of foundation models, while post-training is made by the foundation model engineers. There might be also a difference in a goal.
If you’re building your end-user facing application on top of OpenAI, and you decide to adjust the weights of the existing model, then you do finetuning. At this point, your finetuning will be probably targeting your specific use cases (e.g. domain) of the model to make the model more accurate and knowledgable in your context.
If you’re building your end-user facing application on top of OpenAI, and they decide to adjust the weights of the existing model, then they do post-training. For example, they can apply Reinforcement Learning from Human Feedback (RLHF) to align the model with human values, ethical principles, and intended use cases.
The last phase, post-training, which may use RLHF doesn’t necessarily adjust the weights, but might apply some output filtering techniques. So, training is not always about adjusting the weights?
Sources:
AI Engineering by Chip Huyen (O’Reilly). Copyright 2025 Developer Experience Advisory LLC, 978-1-098-16630-4
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