1. Cursor used online RL to get +28% approval rate: https://cursor.com/blog/tab-rl

2. Vercel used RFT for their AutoFix model for V0: https://vercel.com/blog/v0-composite-model-family

3. Perplexity's Sonar for Deep Research Reasoning I think was a finetuned model: https://docs.perplexity.ai/docs/getting-started/overview

4. Doordash uses LoRA, QLoRA for a "Generalized Attribute Extraction model" https://careersatdoordash.com/blog/unleashing-the-power-of-l...

5. NASA flood water detection https://earthdata.nasa.gov/news/nasa-ibm- openly-release-geospatial-ai-foundation-model-nasa-earth-observation-data6

6. Online RL for robotics - imagine you teaching a robot in the future via some mini finetuning

7. OpenAI's RFT page has more: https://developers.openai.com/api/docs/guides/rft-use-cases

8. For larger models - https://www.mercor.com/blog/expert-data-drives-model-perform...

I just ran a benchmark against haiku of a very simple document classification task that at the moment we farm out to haiku in parallel. very naive same prompt system via same api AWS bedrock, and can see that the a few of the 4b models are pretty good match, and could be easily run locally or just for cheap via a hosted provider. The "how much data and how much improvement" is a question i dont have a good intuition for anymore. I dont even have an order of magnitude guess on those two axis.

Heres raw numbers to spark discussion:

| Model | DocType% | Year% | Subject% | In $/MTok |

|---------------|----------|-------|----------|-----------|

| llama-70b -----| 83 | 98 | 96 | $0.72 |

| gpt-oss-20b --| 83 | 97 | 92 | $0.07 |

| ministral-14b -| 84 | 100 | 90 | $0.20 |

| gemma-4b ----| 75 | 93 | 91 | $0.04 |

| glm-flash-30b -| 83 | 93 | 90 | $0.07 |

| llama-1b ------| 47 | 90 | 58 | $0.10 |

percents are doc type (categorical), year, and subject name match against haiku. just uses the first 4 pages.

in the old world where these were my own in house models, id be interested in seeing if i could uplift those nubmers with traingin, but i haven't done that with the new LLMs in a while. keen to get even a finger to the air if possible.

Can easily generate tens of thousands of examples.

Might try myself, but always keen for an opinion.

_edit for table formatting_

1. If we have robots at home, they need some sort of efficient continual learning, which could be on the go finetuning / RL via some small LoRA - this will need to do multimodal finetuning with sparse reward signals - one could also imagine all data is aggregated to one central processing center after anonymization, and training a larger model with more data + RL like that

2. Agreed images, audio, video etc is what still LoRA does well - the guide at https://unsloth.ai/docs/models/qwen3.5/fine-tune is actually a vision + text finetuning guide, so you can finetune the vision layers on your own use case

3. Model routing is going to be more the norm in the future - ie locally smallish models with LoRA for continuous finetuning can be used, but complex tasks can be offloaded to a large LLM in the cloud.

4. I also wrote about more use-cases below on the post - DoorDash, Vercel, Mercor, Stripe, NASA, Perplexity, Cursor and many others all do finetuning - for eg Cursor, Perplexity finetune large OSS LLMs themselves for their specific product lines - so there is definitely value if you have the data for it.

Because these models are good in general but their Latvian output is half-drivel, like the roots of the words are usually the right ones, but not the rest.

That, and EuroLLM is really slow to release new models that would be similarly good off the shelf.

I make it sound like a rare perfect storm needs to exist to justify fine tuning, but these circumstances are not uncommon - to an extent (a), (c) and (d) were already prerequisites for deploying traditional ML systems.

Using the large model to generate synthetic data offline with the techniques you mentioned, then fine-tuning the small model on it, is an underrated technique.

a) qwen3-coder

b) qwen3.5 (general)