Fine-Tuning and Aligning Large Language Models: A Guide to SFT, RLHF, and What Comes Next

Author(s): M

Originally published on Towards AI.

A beginner-friendly guide.

If you've used ChatGPT, Claude, or any other modern AI assistant, you've used a model that has undergone a complex training process. These models not only learn from large amounts of text (such as web crawls and books), but they also undergo additional steps to ensure they are useful, safe, and aligned with human needs.

The article provides an overview of the advancements in training large language models, focusing on key techniques such as Supervised Fine-Tuning (SFT) and Reinforcement Learning from Human Feedback (RLHF). It delves into the limitations of simple pre-training approaches, highlighting the necessity of alignment techniques to make models more effective and safe for users. The discussion progresses through modern methodologies such as Direct Preference Optimization (DPO), exploring the challenges and future directions in AI training, including the potential of AI-assisted feedback and the growing emphasis on simplicity and efficiency in model development.

Read the full blog for free on Medium.

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Published via Towards AI