Post-Training
Definition
Post-training further adapts a pre-trained model to specific requirements.
A collective term for training steps after the general pre-training of a foundation model, such as supervised instruction tuning, preference optimization, or safety-related adjustment. The exact definition depends on the organization.
Simply explained
After general learning, additional exercises follow, for example for instructions or preferred responses.
Why it matters in practice
The term helps to select, develop, evaluate AI systems in a well-founded manner and realistically assess their technical risks.
Example from business
A base model is adapted to instructions and verified preference data.
Limits and pitfalls
- Equating post-training with a specific procedure or mere inference optimization, attributing all capabilities of this phase to it, or overlooking data provenance and regressions.
Origin & history
Post-training became visible as a distinct development phase with foundation models; it is not a single algorithm and should not be equated with post-training quantization.
Use in mid-sized companies
- Possible application: A base model is adapted to instructions and verified preference data.
Frequently asked questions
What does post-training mean?
A collective term for training steps after the general pre-training of a foundation model, such as supervised instruction tuning, preference optimization, or safety-related adjustment. The exact definition depends on the organization. What mistakes should be avoided? Equating post-training with a specific procedure or mere inference optimization, attributing all capabilities of this phase to it, or overlooking data provenance and regressions.
Sources
Source checked on 10.10.2026. Editorial application examples are hypothetical; no proof of real customer results.
Editorially reviewed on: