Pre-training vs Fine-tuning LLMs: Same knobs, different Game

As more use cases emerge for LLMs, a key challenge has surfaced: adapting a pre-trained model to suit specific requirements. While pre-trained LLMs offer powerful capabilities out of the box, aligning their performance with unique or specialized applications demands additional effort and consideration. Model fine-tuning is one of the key technologies that is gaining prominence. Both pre-training and fine-tuning follow pretty much the same process of deep learning. Are there differences though? This article explores the key differences between pre-training and fine-tuning, specifically on the design goals, process differences and tuning considerations.

Let’s start with the objectives.

Pre-training aims to create a base model from scratch. For LLMs, this requires using a very large corpus of general-purpose training data. The model learns world knowledge, instruction following and reasoning skills. During pre-training, we focus on stable scaling, throughput and general capability across diverse data. Training is budgeted in tokens/steps and instability/divergence is costly.

Fine-tuning aims to customize a pre-trained base model to a specific use case. It improves the accuracy of the base model for use case specific datasets. It optimizes targeted behavior changes without losing core capabilities. Training is budgeted by evaluation improvements and overfitting/catastrophic forgetting are the main risks.

How is deep learning process different between them? The steps are the same, but the focus areas vary.

Hyperparameters are essential for enhancing model accuracy, whether during training or fine-tuning. While the core set of hyperparameters remains consistent, their tuning strategies differ depending on if the process involves pre-training or fine-tuning. The following table provides an overview of commonly used hyperparameters and their respective applications.

https://gist.github.com/kumaranpm/355c810aabe1ddfe37adc6ff126315c6