Fine-tuning LLMs is a waste of time

They provide no references other than self-referencing blogs. It was also suspenseful to read about loss in changing neural network weights when there was 0 mention of quantization. Unfortunately, most of the content in this one was taken from his own previous work.

RAG is getting some backlash and this reads as a backlash of the backlash. I hope things settle down soon but many techfluencers put all their eggs in RAG and used it to gatekeep AI.

> No, I'm just tired of constantly seeing a torrent of misinformation from people who don't know much about how these models actually work nor have done any significant work on their internals, yet try to write about them with authority.

I get that. So what can we do?

One option is when criticizing, write as clearly as possible. Err on the side of overexplaining. From my point of view, it took a back-and-forth for your criticism to become clear.

I'll give an example when more charity and synthesis is welcome:

>> Fine-tuning advanced LLMs isn’t knowledge injection — it’s destructive overwriting. [...] When you fine-tune, you risk erasing valuable existing patterns, leading to unexpected and problematic downstream effects. [...] Instead, use modular methods like [...] adapters.

> This is just incorrect.

"This" is rather unclear. There are many claims in the quote -- which are you saying are incorrect? Possibilities include:

1. "Fine-tuning advanced LLMs isn’t knowledge injection — it’s destructive overwriting."

Sometimes, yes. More often than not? Maybe. Categorically? I'm not sure. [1]

2. "When you fine-tune, you risk erasing valuable existing patterns, leading to unexpected and problematic downstream effects."

Yes, this can happen. Mitigations can reduce the chances.

3. "Instead, use modular methods like [...] adapters."

Your elision dropped some important context. Here's the full quote:

> Instead, use modular methods like retrieval-augmented generation, adapters, or prompt-engineering — these techniques inject new information without damaging the underlying model’s carefully built ecosystem.

This logic is sound, almost out of tautology: the original model is unchanged.

To get more specific: if one's bolted-on LoRA module destroyed some knowledge, one can take that into account and compensate. Perhaps use different LoRA modules for different subtasks then delegate with a mixture of experts? (I haven't experimented with this particular architecture, so maybe it isn't a great example -- but even if it falls flat, this example doesn't undermine the general shape of my argument.)

In summary, after going sentence by sentence, I see one sentence that is dubious, but I don't think it is the same one you would point to.

[1] I don't know if this is considered a "settled" matter. Even if was considered "settled" in ML research, that wouldn't meet my bar -- I have a relativity low opinion of ML research in general (the writing quality, the reproducibility, the experimental setups, the quality of the thinking!, the care put into understanding previous work)

> Don't all these effectively inject new knowledge?

If you mean new knowledge in the sense of "improved weights for a particular task", I guess yes, but the issuing about "new knowledge" is about learning something you didn't know if the first place, rather than being able to more accurately arrive at a conclusion.

1. "excel at a particular task" -> no. A lot of what gets in the way of excelling at a particular task is extraneous knowledge that leads to "thinking" about things that are not relevant about the task. If the job is "hot dog or not hot dog", knowing about the endless "hot dog is a sandwich" debate or the people with hot dog fingers in Everything, Everywhere, All At Once tends to just gets in the way of doing the job as accurately and efficiently as possible.

2. "train on proprietary or sensitive data" -> no. Training on proprietary or sensitive data might not give you any new knowledge, but it may allow for much more refined weights to drive probabilistic decisions. So, if I train on a model with thousands of examples of X-rays of potential cancer patients, it doesn't learn new ideas, but it does learn better weights for determining if it is seeing a tumour.

3. "Complex domain-specific tasks that require advanced reasoning" "Medical diagnosis based on history and diagnostic guidelines" "Determining relevant passages from legal case law"

If you fine tuned an engine to identify species of animals, ones that it is already aware of, you can produce a model that knows with high confidence that "jaguar" is a kind of cat, and not a car or a sports team. It has this high confidence because it knows that, after lots of examples, knowing about there's a car or sports team with that name just gets in the way of making good judgments.

"OpenAI's analogy of training a human in a particular subject until they excel even arguably excludes the possibility of destruction because we don't generally destroy existing knowledge in our minds to learn new things (but some of us may forget the older knowledge over time)."

That is a pretty broad statement about the workings of the human mind. We absolutely do lose sight of neural pathways as our brain learns. I can't remember most of what I learned when I was 2 years old.

> "Fine-tuning LLMs for knowledge injection is a waste of time" is true, but IDK who's trying to do that.

Have people who say this ever actually done it? It works. It works pretty well.

I have no clue why this bad advice is so routinely parroted.

Tool calling is a form of RAG, among the others. This is where MCP is really starting to move this forward.