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Recently, I was on call with an investor who wanted my help in doing due diligence on a startup. During our conversation, they casually mentioned that the startup would be relying on fine-tuning to ensure that their systems were always updated with new information. I was surprised to see the myth of fine-tuning alive and kicking, but I guess Fine Tuning has been chugging on that same immortality juice as GOAT-naldo.
Fine-tuning large language models (LLMs) is frequently sold as a quick, powerful method for injecting new knowledge. On the surface, it makes intuitive sense: feed new data into an already powerful model, tweak its weights, and improve performance on targeted tasks.
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.
In today’s article, we’ll be talking about why Fine-Tuning LLMs is a giant waste of time for Knowledge Injection (90% of what people and think off).
Executive Highlights (TL;DR of the Article)
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