Training
Post-Training
Everything done to a pretrained base model to turn it into a useful assistant.
In depth
Everything done to a pretrained base model to turn it into a useful assistant. That usually means supervised fine-tuning, then preference tuning such as RLHF or DPO, then reinforcement learning on tasks with checkable answers. Post-training uses far less compute than pretraining, but it largely decides instruction following, tone, safety behavior, tool use, and reasoning style. AI2's Tulu 3 published a fully open recipe of SFT, then DPO, then RLVR on top of Llama 3.1 base models.
Primary source: arxiv.org
Example
That usually means supervised fine-tuning, then preference tuning such as RLHF or DPO, then reinforcement learning on tasks with checkable answers.
Go deeper at Developers Digest
Hands-on guides, comparisons, and tutorials that cover Training.
FAQ
Common questions
What is Post-Training?
Everything done to a pretrained base model to turn it into a useful assistant.
Why does Post-Training matter for AI developers?
Post-Training sits in the Training part of the AI stack. Understanding it helps you make better decisions when building, debugging, and shipping AI features.
Where can I learn more about Post-Training?
Developers Digest publishes tutorials and videos that cover Training topics including Post-Training. Check the blog and YouTube channel for hands-on walkthroughs.
Related
Related terms
Training
Supervised Fine-Tuning (SFT)
Fine-tuning a pretrained model on curated prompt and response pairs so it learns to imitate the demonstrated answers.
Training
PEFT (Parameter-Efficient Fine-Tuning)
A family of fine-tuning methods that adapt a large pretrained model by training only a small number of new or selected parameters while the original weights stay frozen.
Training
RLHF (Reinforcement Learning from Human Feedback)
A training technique that fine-tunes a model using human preference judgments.
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