Offline learning in machine learning refers to a training approach where a model is trained on a fixed dataset that is not updated during the learning process[1]. This method, also known as batch learning, involves training the model using all available data at once, rather than incrementally updating it as new data becomes available[2][5].

In offline learning:

1. The entire dataset is used for training at once[2].
2. The model's parameters are updated after processing the complete dataset[2].
3. Once trained, the model remains static until retrained on new data[5].

Relation to Supervised Fine-Tuning (SFT)

Supervised Fine-Tuning (SFT) is a technique often used in transfer learning, particularly for large language models. It shares some similarities with offline learning:

1. Fixed Dataset: Both SFT and offline learning typically use a predetermined dataset for training[1][5].

2. Batch Processing: SFT, like offline learning, often processes data in batches to update the model's parameters[2].

3. Model Stability: After training, both approaches result in a static model that doesn't automatically adapt to new data without retraining[5].

However, SFT differs from traditional offline learning in some key aspects:

1. Pre-trained Base: SFT starts with a pre-trained model and fine-tunes it for specific tasks, while offline learning may train a model from scratch[1].

2. Task Specificity: SFT is often used to adapt a general model to specific tasks or domains, whereas offline learning can be used for various types of models and tasks[2].

3. Data Efficiency: SFT can often achieve good results with smaller datasets compared to training a model from scratch, making it more data-efficient in many cases[5].

In the context of AI development, offline learning and SFT are both valuable approaches, with SFT being particularly useful for adapting large, pre-trained models to specific applications while maintaining the benefits of offline learning's simplicity and stability.

Citations: [1] https://en.wikipedia.org/wiki/Offline_learning [2] https://www.baeldung.com/cs/online-vs-offline-learning [3] https://www.qwak.com/post/online-vs-offline-machine-learning-whats-the-difference [4] https://stats.stackexchange.com/questions/897/online-vs-offline-learning [5] https://www.geeksforgeeks.org/batch-offline-learning-vs-online-learning-in-artificial-intelligence/ [6] https://learn.microsoft.com/zh-tw/dotnet/machine-learning/mldotnet-api [7] https://developers.google.com/machine-learning/glossary [8] https://www.nettechindia.com/machine-learning-course