The case for the return of fine-tuning
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The case for the return of fine-tuning
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Re: The case for the return of fine-tuning
#2The 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
Re: The case for the return of fine-tuning
#3This 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!
Re: The case for the return of fine-tuning
#4Fine tuning by pretraining over a RL tuned model is dumb AF. RL task tuning works quite well.
Re: The case for the return of fine-tuning
#5This 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!
Magic of a CDN? Plus avoiding JS probably. Haven't checked source though.
Re: The case for the return of fine-tuning
#6Fine-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.
Re: The case for the return of fine-tuning
#7Fine tuning by pretraining over a RL tuned model is dumb AF. RL task tuning works quite well.
You may have no choice in how the model you are fine tuning was trained, and may have no interest in verticals it was RL tuned for.
In any case, platforms like tinker.ai support both SFT and RL.
Re: The case for the return of fine-tuning
#8[deleted]
Re: The case for the return of fine-tuning
#9The 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-opti…
They don't offer it for GPT-5 series, as a result much of the time fine-tuning Gemini 2.5-Flash is a better deal.
Re: The case for the return of fine-tuning
#10A 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...