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A language model out of the box has read a large share of the internet and has never worked a day at your company. A system prompt is a note stuck to its monitor. It reads the note before every reply and still makes the same mistake every day, because a note does not change what it knows how to do. Training does. Training shows the model examples of the job done right and adjusts its weights, so the next reply comes out more like those examples without anyone telling it to.

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The mechanism

The first training, on the internet, is called pretraining. It gives the model language and general knowledge. Everything you do to the model after that is post-training. That is the part this library is for. There are two families of post-training, and you will use both. SFT teaches what a good reply looks like. RL teaches the model to tell its own good replies from its bad ones, which is how it learns things nobody wrote an example for. Both need the same two ingredients: rows to learn from, and a score that says which rows are good. Making those two things well, and proving they worked, is the whole job.
A prompt edit and a training run are not rivals. Edit the prompt when the fix is a fact the model did not have. Train when the fix is a habit: the model has the facts and still does the wrong thing.

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Run it

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Where it comes from

  1. Ouyang, L. et al. Training Language Models to Follow Instructions with Human Feedback. NeurIPS, 2022. arXiv:2203.02155. The SFT-then-RL recipe most chat models follow.
  2. Lambert, N. Reinforcement Learning from Human Feedback. arXiv:2504.12501, 2025. Chapters Instruction Fine-Tuning and Reinforcement Learning.
  3. Lambert, N. et al. Tülu 3: Pushing Frontiers in Open Language Model Post-Training. arXiv:2411.15124, 2024. A full open post-training run, SFT then preference tuning then RL.

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Next

One saved conversation is a rollout: where the rows come from.
Last modified on September 19, 2026