For me, trying to fine-tune a model to write "best day" prose I would accept over 80% of the time.

You are correct if we are talking about knowledge.

However it is bad at hyper-idiosyncratic, gritty style transfer.

I first noticed the issue when asking claude code to draft email responses. The choice of register was off. ("Register in writing refers to the level of formality and tone chosen to suit a specific audience, purpose, and context.")

I decided to talk all my HN comments and rewrite them in various bad LLM prose, and see if I could use DSPy to optimize a prompt using in-context-learning (ICL, I give it 10 examples of my HN comments) and the results were abysmal. RHLF fine-tuned frontier LLMs have a deep seated aversion to the target stylistic distribution of my comments.

I tried fine-tuning qwen3, llama, and gemma models. Instruct models are already so tuned that they could not be tuned. This is using several hunded comments as gold targets and 5 different LLM degradations per gold as the input.

How well would you say it worked? I do like the idea of taking my historical forum posts and e-mails and whatnot and training an autocomplete LLM that is specifically "my voice".