Post-SFT Alignment with DPO and GRPO : How to Fine-Tune Correctly, Part 6

Supervised Fine-Tuning (SFT) reaches its limits when models fail to produce varied and contextually appropriate responses in ambiguous situations. Post-SFT alignment methods like Direct Preference Optimization (DPO) and Group Relative Policy Optimization (GRPO) address these issues by teaching models to prefer better outputs or follow deterministic scoring functions, respectively.

Developers should focus on optimizing DPO parameters and ensuring high-quality preference data to improve model behavior beyond SFT limitations.

Read the full article at Towards AI - Medium

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