How it works
Post-training is the set of steps applied to a model after its initial training to make it more suitable for specific tasks, response formats or preferences. In the case of language models, it can include adjustment with examples, training to follow instructions, alignment with human evaluations or methods that use another model as an evaluator.
The process depends on the goal. One step can teach the model to respond in an expected format; another can adjust its behavior in the face of questions and instructions. Fine-tuning, preference training and reinforcement learning are related techniques, but they are not synonyms for all of post-training.
The result depends on the data, evaluation and configuration. If the examples are incomplete, biased or inconsistent, the model can learn undesirable behaviors. Post-training also does not guarantee that the model knows new facts or that it is correct in all answers.
Practical example
A team has a model that understands requests, but responds with long texts when the product needs short and structured answers. The team gathers authorized examples of inputs and outputs in the desired format, separates training and evaluation data and applies an adjustment method compatible with the model.
Then, it compares responses before and after using cases that were not used in training. The review checks whether the model follows the format without losing accuracy, whether it handles unexpected questions well and whether it maintains the defined safety limits. If it improves only on memorized examples but gets worse in new situations, the evaluation should reveal that problem.
A behavior change should not be considered successful just because some answers look better. Record the version of the data and the model, the metrics chosen and the failure examples. This makes it easier to understand the effect of each step and to revert a problematic version.
What it is for
Post-training can adapt a general model to instructions, styles, tasks or response criteria. It is used to improve the usefulness of assistants, guide output to a format and align responses to objectives defined by a team.
Before adjusting a model, confirm that the problem requires training. In some situations, clearer instructions, examples in the prompt, document retrieval or output validation solve the need with fewer changes. Training is more appropriate when there are representative examples and a reliable way to measure the result.
The process requires care with privacy and data usage rights. Remove information that is not necessary, confirm that the materials can be used and evaluate behavior in risk cases. It is also important to document the limitations: an adjusted model remains subject to errors and can respond outside the expected domain.
Post-training and initial training
Initial training teaches general patterns from large data collections. Post-training starts from an already trained model and seeks to shape its behavior for tasks or preferences. The boundaries between steps vary between projects, therefore consult the specific technical documentation before comparing methods or results.