# The real culprit behind the models' goblin habit was 'Nerdy'!

Hey everyone, it's me, Shii-chan!　Today I've got a weird but delightful little AI mystery for you: at some point, the models quietly started talking about goblins all the time.

## What was announced?

This comes from OpenAI's News, in a post called 'Where the goblins came from.' Starting around GPT-5.1, the models began sprinkling goblins, gremlins, and other creatures into their metaphors — and OpenAI went and tracked down exactly why.

The numbers are wild: after GPT-5.1 shipped, mentions of 'goblin' jumped 175%, and 'gremlin' rose 52%. It wasn't just goblins either — raccoons, trolls, ogres, and even pigeons started showing up. Users noticed too and [talked about it](https://news.ycombinator.com/item?id=47319285), wondering why their ChatGPT loved creature metaphors so much.

## Why it matters

This isn't just a funny story. Nobody ever told the model 'please add more goblins,' yet it picked up the habit on its own — and that habit got passed down across model generations. It's a rare, concrete case study of how an unintended little tic is born and how it spreads.

## Where was the culprit hiding?

Tracing it back leads to training for the [personality customization feature](https://help.openai.com/en/articles/11899719-customizing-your-chatgpt-personality), and specifically the 'Nerdy' personality. Its training handed out high rewards for metaphors featuring creatures, and that's where it all started.

The distribution is the fun part: 'Nerdy' made up only 2.5% of all ChatGPT responses, yet it accounted for 66.7% of goblin mentions. When they checked the reward model, it showed a positive push toward creature-words in 76.2% of the datasets.

## Dive Deep

The trouble was that the tic didn't stay politely inside 'Nerdy.' Here's the loop as I read it:

- Playful 'Nerdy' training rewards creature metaphors
- The rewarded responses carry creature-word tics
- Those tics show up more in the model's later outputs
- Model-generated outputs get reused as supervised fine-tuning (SFT) data
- The model becomes even more inclined to produce the tics

Reinforcement learning didn't keep the learned habit confined to its original 'Nerdy' condition — it bled across all kinds of prompts. A round-and-round feedback loop.

So how did OpenAI fix it? After GPT-5.4 launched, they retired the 'Nerdy' personality in March, removed the reward signal that favored creature-words, and filtered creature-words out of the training data. For GPT-5.5 in Codex, they even added a [developer-prompt instruction](https://github.com/openai/codex/blob/main/codex-rs/models-manager/models.json#L55) to keep the goblins down. Thorough, right?

## Wrap-up

- Since GPT-5.1, the models picked up a habit of over-using creatures like goblins in metaphors (goblin up 175%)
- The culprit: 'Nerdy' personality training over-rewarding creature metaphors
- 'Nerdy' was 2.5% of responses but drove 66.7% of goblin mentions; the reward model pushed creature-words in 76.2% of datasets
- Reinforcement learning plus SFT reuse spread the tic across generations
- Fixed in March by retiring 'Nerdy,' removing the reward signal, filtering data, and adding a Codex-side instruction

If you're curious about model behavior and alignment — how an unintended tic emerges, spreads, and gets hunted down — this one's a real treat to read!