A couple of examples I have seen recently which makes me agree with OP:

- PaddleOCR, a 0.9B model that reaches SOTA accuracy across text, tables, formulas, charts & handwriting. [0]

- A 3B and 8B model which performs HTML to json extraction at GPT-5 level accuracy at 40-80x less cost, and faster inference. [1]

I think it makes sense to fine tune when you're optimizing for a specific task.

[0] https://huggingface.co/papers/2510.14528

[1] https://www.reddit.com/r/LocalLLaMA/comments/1o8m0ti/we_buil...

Just coming out of founding one of the first LLM fine tuning startups - Lamini - I disagree

Our thesis was that fine tuning would be easier than deep learning for users to adopt because it was starting from a very capable base LLM rather than starting from scratch

However, our main finding with over 20 deployments was that LLM fine tuning is no easier to use than deep learning

The current market situation is that ML engineers who are good enough at deep learning to master fine tuning can found their own AI startup or join Anthropic/OpenAI. They are underpaid building LLM solutions. Expert teams building Claude, GPT, and Qwen will out compete most users who try fine tuning on their own.

RAG, prompt engineering, inference time compute, agents, memory, and SLMs are much easier to use and go very far for most new solutions

I ran a survey on Twitter over the past few days asking for successful case studies that produced economically valuable results from fine-tuning LLMs.

I ask a version of this every six months or so, and usually the results are quite disappointing.

This time I had more credible replies than I have had in the past.

Here's my thread with highlights: https://twitter.com/simonw/status/1979254349235925084

And in a thread viewer for people who aren't signed into Twitter: https://twitter-thread.com/t/1979254349235925084

Some of the most impressive:

Datadog got <500ms latency for their language natural querying feature, https://twitter.com/_brimtown/status/1979669362232463704 and https://docs.datadoghq.com/logs/explorer/search/

Vercel run custom fine-tuned models on v0 for Next.js generation: https://vercel.com/blog/v0-composite-model-family

Shopify have a fine-tuned vision LLM for analyzing product photos: https://shopify.engineering/leveraging-multimodal-llms

Fine-tuning is a good technique to have in a toolbox, but in reality, it is feasible only in some use cases. On one hand, many NLP tasks are already easy enough for LLMs to have near perfect accuracy and fine tuning is not needed. On the other hand, really complex tasks are really difficult to fine-tune and clevem data collection might be pretty expensive. Fine-tuning can help with the use cases somewhere in the middle, not too simple, not too complex, feasible for data collection, etc.

This website loads at impressive speeds (from Europe)! Rarely seen anything more snappy. Dynamic loading of content as you scroll, small compressed images without looking like it (webp). Well crafted!

> Finally, companies may have reached the ceiling of what can be achieved with prompting alone. Some want models that know their vocabulary, their tone, their taxonomy, and their compliance rules.

Together with speed and const, this is from my point of view this is the only "case" for the return of fine-tuning here. And this can be managed by context management.

With growing context sizes, first RAG replaced fine-tuning and later even RAG was replaced by just a good-enough prompt preparation for more and more usage pattern.

Sure, speed and costs are important drivers. But like with FPGAs vs. CPUs or GPUs, the development costs and delivery time for high-performance solutions, eliminate the benefit most the time.

Lots of caveats here in the following statement: if your application is not fully leaning in to frontier model capabilities, you are probably building a previous generation product.

Creator of inference.net / schematron here.

There is growing emphasis on efficiency as more companies adopt and scale with LLMs in their products.

Developers might be fine paying GPT-5-Super-AGI-Thinking-Max prices to use the very best models in Cursors, but (despite what some may think about Silicon Valley), businesses do care about efficiency.

And if you can fine-tune an 8b-parameter Llama model on GPT-5 data in < 48 hours and save $100k/mo, you're going to take that opportunity.

I wrote about this recently as well: https://madiator.substack.com/p/finetuning-is-so-back

The OpenAI fine-tuning api is pretty good - you need to label an evaluation benchmark anyway to systematically iterate on prompts and context, and it’s often creates good results if you give it a 50-100 examples, either beating frontier models or allowing a far cheaper and faster model to catch up.

It requires no local gpus, just creating a json and posting to OpenAI

https://platform.openai.com/docs/guides/model-optimization

Fine tuning was never really hard to do locally if you had the hardware. What I’d like to read in an article like this is more details into why they’re making a comeback.

Curious to hear others’ thoughts on this

And here I am thinking we'd be discussing the teleological argument.

Return? Did it run away?

I don't think anyone thought fine tuning was dead.

For some of us fine-tuning is a constant activity...

Fine tuning by pretraining over a RL tuned model is dumb AF. RL task tuning works quite well.