What is Post-training?
It is the improvement process performed for a model that has completed its basic training to perform better on specific tasks.
Overview
After pre-training, where the model gains its general capabilities, this is an additional stage performed for the model to adopt a specific style or specialize in a particular subject. During this process, the model is trained with higher-quality data and refined to remove its errors.
How it works
The model is retrained using methods such as RLHF or specific datasets. This ensures it provides safer, more accurate, and more useful responses.
Where it is used
It is used in the preparation stage of large language models before they are presented to the end user.
Commonly confused with
It is confused with pre-training; while pre-training lays the foundations of the model, post-training shapes the behavior of the model.
Frequently asked questions
Why is post-training necessary?
It is necessary to fine-tune the model's behaviors so that it provides safer and more user-friendly responses.
Related terms
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This explanation was written in plain language for TreScout and machine-translated from the Turkish original · the Turkish version prevails. If something looks wrong or missing, write to hello@trescout.com. Read in Turkish →