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Pre-training uses massive datasets and computational resources—often thousands of GPUs running for weeks or months—making it a domain dominated by top AI companies. Post-training is much lighter in cost and time (often days instead of months) and foc...

Turning a base model into a reasoning model is essentially a post-training + data problem. The model’s architecture can stay the same — what changes is how it’s fine-tuned, what data it sees, and what training objectives you use. Here’s the typical p...
