Difference Between Supervised Fine-Tuning (SFT) And Reinforcement Learning From Human Feedback (RLHF)

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Difference Between Supervised Fine-Tuning (SFT) And Reinforcement Learning From Human Feedback (RLHF)

amit155

@amit155

October 21, 2024

Data Science & AI Community AI Inside

The difference between Supervised Fine-Tuning (SFT) and Reinforcement Learning from Human Feedback (RLHF) lies in their methodologies and purposes in training machine learning models, particularly in natural language processing.

Supervised Fine-Tuning (SFT)
SFT is a process where a pre-trained model is further trained on a labeled dataset. The labels guide the model to learn specific tasks, such as classification or translation.

Data Type: SFT relies on a static dataset with clear, predefined labels. The training process involves adjusting the model’s weights based on the differences between predicted outputs and actual labels.

Objective: The goal is to minimize prediction error and improve performance on specific tasks. This method is effective for tasks with clear input-output pairs.

Example: Fine-tuning a language model on a dataset of questions and answers to enhance its ability to answer similar questions accurately.

Reinforcement Learning from Human Feedback (RLHF)
RLHF involves using human feedback to guide the training of a model, often in a reinforcement learning framework. It focuses on maximizing long-term reward based on human evaluations.

Data Type: Instead of static labels, Vaidik AI RLHF Services utilizes feedback from human evaluators to shape the model's behavior over time. This feedback can be qualitative and can cover various aspects of model performance.

Objective: The aim is to align the model's outputs with human preferences and improve its ability to perform tasks that may not have clear-cut answers. It emphasizes learning from interactions and outcomes rather than just input-output pairs.

Example: Training a conversational agent using human ratings to refine its responses based on which replies are more engaging or helpful.

Summary
In essence, SFT is about optimizing a model using labeled data for specific tasks, while RLHF focuses on aligning a model’s behavior with human values and preferences through feedback. Both methods are crucial in developing AI systems that are accurate and aligned with user expectations.

RLHF and SFT

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