Fine-tuning LLMs is a waste of time
codinginterviewsmadesimple.substack.com
Fine-tuning LLMs is a waste of time
1–10 of 94 posts
Re: Fine-tuning LLMs is a waste of time
#2"But this logic breaks down for advanced models, and badly so. At high performance, fine-tuning isn’t merely adding new data — it’s overwriting existing knowledge. Every neuron updated risks losing information that’s already intricately woven into the network. In short: neurons are valuable, finite resources. Updating them isn’t a costless act; it’s a dangerous trade-off that threatens the delicate ecosystem of an advanced model."
Mainly including this article to spark discussion—I agree with some of this and not with all of it. But it is an interesting take.
Re: Fine-tuning LLMs is a waste of time
#3[deleted]
Re: Fine-tuning LLMs is a waste of time
#4I think of it as trying to encourage the LLM to want to give answers from a particular part of the phase space. You can do it by fine tuning it to be more likely to return values from there, or you can prompt it to get into that part of the phase space. Either works, but fiddling around with prompts doesn't require all that much MLops or compute power.
That said, fine tuning small models because you have to power through vast amounts of data where a larger model might be cost ineffective -- that's completely sensible, and not really mentioned in the article.
Re: Fine-tuning LLMs is a waste of time
#5"But this logic breaks down for advanced models, and badly so. At high performance, fine-tuning isn’t merely adding new data — it’s overwriting existing knowledge. Every neuron updated risks losing information that’s already intricately woven into the network. In short: neurons are valuable, finite resources. Updating them isn’t a costless act; it’s a dangerous trade-off that threatens the delicate ecosystem of an ad…
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Re: Fine-tuning LLMs is a waste of time
#6"Fine-tuning large language models (LLMs) is frequently sold as a quick, powerful method for injecting new knowledge"
Is that true though? I don't think I've seen a vendor selling that as a benefit of fine-tuning.
Re: Fine-tuning LLMs is a waste of time
#7"Fine-tuning large language models (LLMs) is frequently sold as a quick, powerful method for injecting new knowledge" Is that true though? I don't think I've seen a vendor selling that as a benefit of fine-tuning.
Yeah, as soon as I read that I felt like the author was living in a very different context from mine. It's never even occurred to me that fine-tuning could be an effective method for injecting new knowledge.
If anything, I expect fine-tuning to destroy knowledge (and reasoning), which hopefully (if you did your fine-tuning right) is not relevant to the particular context you are fine-tuning for.
Re: Fine-tuning LLMs is a waste of time
#8I think of it as trying to encourage the LLM to want to give answers from a particular part of the phase space. You can do it by fine tuning it to be more likely to return values from there, or you can prompt it to get into that part of the phase space. Either works, but fiddling around with prompts doesn't require all that much MLops or compute power. That said, fine tuning small models because you have to power thr…
> That said, fine tuning small models because you have to power through vast amounts of data where a larger model might be cost ineffective -- that's completely sensible, and not really mentioned in the article.
...which I thought was arguably the most popular use case for fine tuning these days.
Re: Fine-tuning LLMs is a waste of time
#9Clickbait headline. "Fine-tuning LLMs for knowledge injection is a waste of time" is true, but IDK who's trying to do that. Fine-tuning is great for changing model behavior (i.e. the zillions of uncensored models on Hugging Face are much more willing to respond to... dodgy... prompts than any amount of RAG is gonna get you), and RAG is great for knowledge injection.
Also... "LoRA" as a replacement for finetuning??? LoRA is a kind of finetuning! In the research community it's actually referred to as "parameter efficient finetuning." You're changing a smaller number of weights, but you're still changing them.
Re: Fine-tuning LLMs is a waste of time
#10I think of it as trying to encourage the LLM to want to give answers from a particular part of the phase space. You can do it by fine tuning it to be more likely to return values from there, or you can prompt it to get into that part of the phase space. Either works, but fiddling around with prompts doesn't require all that much MLops or compute power. That said, fine tuning small models because you have to power thr…
> That said, fine tuning small models
Mostly referred to as model distillation, but I give the author the benefit of the doubt that they didn't mean that.