• fine-tuning — Taking a trained model and training it a bit more on your own examples so it gets better at one specific job.
• chain-of-thought — Having a model write out intermediate reasoning steps before its answer, which markedly improves performance on hard problems.

Ordering the fine-tuning stages matters: reasoning traces first, then code-executed steps, which separates logical decomposition from arithmetic that language models get wrong. Useful if you are tuning a small model for quantitative tasks.

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