Post-training refers to the process by which a model continues to become more useful, stable, and in line with the target task through additional training steps after completing large-scale pre-training. Many people mention whether the model is strong or not, and their first reaction is to focus on the amount of pre-training data and the scale of parameters, but now the industry is seeing more and more clearly that it is often post-training that really turns "memorizing knowledge" into "being able to do a good job".
Pre-training is more like laying a foundation, allowing the model to learn language patterns, knowledge distribution, and world associations; Post-training is more like decoration and tuning, letting it know how to answer, when to refuse, how to be closer to human preferences, and how to perform on specific tasks. Because of this, the difference felt by the user in the end can be very large in two models with close bases, and the difference comes from post-training many times.
There is no one way to do post-training. The most common include supervised fine-tuning, which allows the model to learn high-quality examples; preference optimization to bring the model closer to the way humans like to answer; and special training around inference, tool calling, and security boundaries. After the popularity of inference models, the words RLHF and RLVR appeared frequently, which are essentially different paths in the category of post-training.
Why is everyone paying so much attention to it now? Because the competition of large models is no longer just "who eats more corpus". Pre-training is becoming more and more expensive, and it is getting closer and closer to the head resource war; Then training directly determines the product experience. Whether the user perceives stability, whether he is obedient, whether he can call tools, whether he can reason in multiple steps, and whether he can make up blindly, many of them are not visible at a glance from the base parameters, but the results of post-training.
However, post-training also comes at a cost. It introduces a target bias. You have enhanced security, and the model may be more conservative; If you reinforce code or math, the general chat style may change; You may also pay higher inference costs to make the model more "thinking". Therefore, post-training is not about the more the better, but whether the goal is clear, whether the data is clean, and whether the assessment can keep up.
Another common misunderstanding is to understand post-training as "supplementing knowledge to the model". It may certainly lead to an increase in capabilities, but the core role is often not to expand the fact base, but to adjust behavior. It determines how the model organizes answers, makes trade-offs, and faces boundary situations. In other words, it is more like behavioral shaping than just memory add-on.
Today, many models are starting to emphasize post-training more when they are released, which actually shows that the industry's focus has changed. Everyone no longer only looks at who has a big foundation, but more about who can make the base into a truly usable, controllable, and online system. This is why many models really widen the gap, not in the pre-training stage, but in the post-training stage.
