Why do LLMs code better than they talk?

Why's it so hard to get LLMs to embody different personas or respond in a way with less patterns or agree-ability than it is to have them write code in a variety of languages? I always thought it was odd based on the variety of data they seem to be trained on.

If I'm missing a config or something feel free to tell me.

EDIT: By better I mean, more free to respond naturally, disagree, critique, affirm appropriately, ask questions naturally, talk outside of its HR structure, etc. Why do they always sound like willing assistants with a limited vocabulary rather than an omniscient "knowing" thing given all the text data its trained on.

Some answers I've gotten:
- Reinforcement learning works better with Code. Code is verifiable. Most of the training data is biased towards it. There's less verifiability in human speech despite the volume of verifiable examples.
- Companies want to nerf the model so it speaks less out of bounds and bias it with affirmative speaking for the sake of retaining people.