alt Hacker News> LLMs seem to get tragically stuck on certain patterns.
That is likely an artifact of the fine-tuning process:
> Once a style tic is rewarded, later training can spread or reinforce it elsewhere, especially if those outputs are reused in supervised fine-tuning or preference data.
> That creates a feedback loop:
> * Some rewarded examples contain a distinctive lexical tic.
> * The tic appears more often in rollouts.
> * Model-generated rollouts are used for supervised fine-tuning (SFT).
> * The model gets even more comfortable producing the tic.