What happens after LLM pretraining?

After LLM pretraining, the model is usually a base model that has learned broad language patterns but is not yet optimized for following instructions or behaving like a polished assistant.

The next stages depend on how the model will be used.

1. Evaluate the pretrained model

Developers first check whether pretraining produced the expected capabilities.

Evaluation may look at areas such as:

• Language modeling quality
• Knowledge coverage
• Reasoning tasks
• Code ability
• Multilingual performance
• Safety-related behavior
• Domain-specific performance

This helps identify strengths, weaknesses, and problems before further training.

2. Fine-tune the model

The pretrained model may then be trained further on a smaller, more targeted dataset.

This is called fine-tuning.

For example, the model might be adapted for:

• Instruction following
• Coding
• Customer support
• Legal text
• Medical language
• A particular organization or domain

Fine-tuning starts from the pretrained parameters rather than training a new model from scratch.

3. Perform supervised fine-tuning

For assistant-style models, a common next step is supervised fine-tuning (SFT).

The model is trained on examples such as:

Instruction:
Explain photosynthesis simply.

Desired response:
Photosynthesis is the process...

This teaches the model what useful instruction-following behavior should look like.

4. Apply preference or alignment training

After SFT, the model may go through additional training using preference data.

Humans or other systems may compare responses and indicate which ones are better.

Methods such as:

• RLHF
• DPO
• Other preference-optimization techniques

can then be used to make the model more helpful, safe, consistent, or aligned with desired behavior.

5. Perform safety testing and refinement

Before deployment, models are usually tested for issues such as:

• Harmful outputs
• Prompt injection behavior
• Hallucinations
• Bias
• Policy violations
• Reliability problems

Additional training, filtering, system-level safeguards, or evaluation may follow.

6. Optimize the model for inference

A model that works well during training may still be expensive to serve.

Deployment preparation can include:

• Quantization
• Distillation
• KV-cache optimization
• Faster attention implementations
• Batching
• Model parallelism
• Serving-system optimization

These techniques aim to reduce latency, memory use, and cost.

7. Deploy and monitor the model

After post-training and optimization, the model can be deployed in an application.

The full lifecycle may look like:

pretraining
    ↓
base model
    ↓
evaluation
    ↓
fine-tuning / instruction tuning
    ↓
preference alignment
    ↓
safety testing
    ↓
inference optimization
    ↓
deployment

The exact sequence varies between model developers.

Does every model go through all of these stages?

No.

A research base model may stop after pretraining and evaluation.

A production assistant may go through several rounds of fine-tuning, preference training, safety evaluation, and deployment optimization.

For the next stage in detail, see “What is LLM fine-tuning?”

In short: after pretraining, an LLM typically becomes a base model that is evaluated and then may undergo fine-tuning, instruction tuning, preference alignment, safety testing, and inference optimization before deployment.