Fine-Tuning Mastery: Selecting SFT, DPO, RFT, or Vision – Featuring a DPO Deep Dive

Discover OpenAI's fine-tuning methods to boost your models for specific tasks. Learn when to use Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), Reinforcement Fine-Tuning (RFT), or Vision Fine-Tuning, with an in-depth DPO guide to get you started.

This guide targets developers and machine learning practitioners with experience using OpenAI’s APIs, who want to apply fine-tuned models in research or suitable scenarios. OpenAI’s services are not meant for personalized medical treatment or diagnosis and are bound by our relevant terms.

This guide explores the fine-tuning approaches available on the OpenAI platform, focusing on their ideal applications and limitations to guide you toward the best fit for your needs. It offers a detailed examination of one key method—Direct Preference Optimization (DPO)—along with references to resources for the others.

What is fine-tuning? Fine-tuning involves further training a model on a targeted, domain-specific dataset to tailor it for particular tasks. We typically fine-tune for two primary goals:

• Enhance performance on targeted tasks
• Boost efficiency (e.g., fewer tokens required, distilling knowledge into compact models)

The OpenAI platform currently offers four fine-tuning options:

• Supervised fine-tuning (SFT): This method applies classic supervised learning with input-output pairs to refine model parameters. Training minimizes the gap between predicted and desired outputs in the given examples, enabling the model to mirror patterns from those pairs.

• Vision fine-tuning: This builds on supervised fine-tuning for multimodal inputs, handling text and images together in a cohesive setup. Training reduces errors in text-image pairs, strengthening the model's grasp of visual data.

• Direct preference optimization (DPO): This approach leverages pairwise comparisons (e.g., preferred vs. rejected responses) to train the model to prioritize desired outputs. It captures and replicates the preference trends in the comparison dataset.

• Reinforcement fine-tuning (RFT): This uses reinforcement learning driven by a reward mechanism (from a grader or reward model) to align the model with intricate goals. During training, the model produces responses to prompts, which are scored for quality; parameters update to boost rewards, promoting superior reasoning or decision-making via ongoing feedback.