The essence of it is that after the "read the whole internet and predict the next token" pre-training step (and the chat fine-tuning), SotA LLMs now have a training step where they solve huge numbers of tasks that have verifiable answers (especially programming and math). The model therefore gets the very broad general knowledge and natural language abilities from pre-training and gets good at solving actual problems (problems that can't be bullshitted or hallucinated through because they have some verifiable right answer) from the RL step. In ways that still aren't really understood, it develops internal models of mathematics and coding that allow it to generalize to solve things it hasn't seen before. That is why LLMs got so much better at coding in 2025; the success of tools like Claude Code (to pick just one example) is built upon it. Of course, the LLMs still have a lot of limitations (the internal models are not perfect and aren't like how humans think at all), but RL has taken us pretty far.
Unfortunately the really interesting details of this are mostly secret sauce stuff locked up inside the big AI labs. But there are still people who know far more than I do who do post about it, e.g. Andrej Karpathy discusses RL a bit in his 2025 LLMs Year in Review: https://karpathy.bearblog.dev/year-in-review-2025/
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The essence of it is that after the "read the whole internet and predict the next token" pre-training step (and the chat fine-tuning), SotA LLMs now have a training step where they solve huge numbers of tasks that have verifiable answers (especially programming and math). The model therefore gets the very broad general knowledge and natural language abilities from pre-training and gets good at solving actual problems (problems that can't be bullshitted or hallucinated through because they have some verifiable right answer) from the RL step. In ways that still aren't really understood, it develops internal models of mathematics and coding that allow it to generalize to solve things it hasn't seen before. That is why LLMs got so much better at coding in 2025; the success of tools like Claude Code (to pick just one example) is built upon it. Of course, the LLMs still have a lot of limitations (the internal models are not perfect and aren't like how humans think at all), but RL has taken us pretty far.
Unfortunately the really interesting details of this are mostly secret sauce stuff locked up inside the big AI labs. But there are still people who know far more than I do who do post about it, e.g. Andrej Karpathy discusses RL a bit in his 2025 LLMs Year in Review: https://karpathy.bearblog.dev/year-in-review-2025/