One theory is that the knowledge required to solve the task is already stored in the parameters of the model, and only the style has to change for task success

In particular, learning to generate longer outputs may be possible in few parameters

Reminded me of: https://arxiv.org/abs/2501.19393

we develop budget forcing to control test-time compute by forcefully terminating the model’s thinking process or lengthening it by appending “Wait” multiple times to the model’s generation when it tries to end. This can lead the model to double-check its answer, often fixing incorrect reasoning steps

Maybe, indeed, the model simply learns to insert the EOS token (or similar) later, and the capability is already in the base model