Comment by ankit219

I see this and immediately relived the last two years of the journey. I think some of the mental model that helped me might help the community too.

What people expect from finetuning is knowledge addition. You want to keep the styling[1] of the original model, just add new knowledge points that would help your task. In context learning is one example of how this works well. Just that even here, if the context is out of distribution, a model does not "understand" it and would produce guesswork.

When it comes to LoRA or PEFT or adapters, it's about style transfer. And if you focus on a specific style of content, you will see the gains, just that the model wont learn new knowledge that wasnt already in original training data. It will forget previously learnt styles depending on context. When you do full finetuning (or SFT with no frozen parameters), it will alter all the parameters, and results in gain of new knowledge at the cost of previous knowledge (and would give you some gibberish if you ask about topics outside of domain). This is called catastrophic forgetting. Hence, yes, full finetuning works - just that it is an imperfect solution like all the others. Recently, with Reinforcement learning, there have been talks of continual learning, where Richard sutton's latest paper also lands at, but thats at research level.

Having said all that, if you start with the wrong mental model for Finetuning, you would be disappointed with the results.

The problem to solve is about adding new knowledge, while preserving the original pretrained intelligence. Still in wip, but we published a paper last year on one way it could be done. Here is the link: https://arxiv.org/abs/2409.17171 (it also has results for experiments all different approaches).

[1]: Styling here means the style learned by the model in SFT. Eg: Bullets, lists, bolding out different headings etc. all of that makes the content readable. The understanding of how to present the answer to a specific question.