Originally published on AI Tech Connect.
What this guide gives you Most teams that fine-tune an open-weight model stop after supervised fine-tuning, then wonder why the model is technically correct but somehow off — too terse or too waffly, agreeable when it should push back, willing to answer things it should decline. The reason is structural. Supervised fine-tuning teaches the model to imitate one good completion per prompt. It never sees a worse completion, so it never learns that one answer is better than another. Preference tuning is the step that closes that gap, and in 2026 it is the standard practice for aligning open-weight models after SFT. This is a recipe you can keep and reuse. The methods are stable: preference pairs of chosen and rejected responses, Direct Preference Optimisation (DPO) against a frozen reference,…
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