Episode 11- Shaping Model Behavior in GPT-5.1

Hello, I'm Andrew Main and this is the OpenAI Podcast. Today our guests are Christina Kim, who's a research lead working on post training at OpenAI and Laurentia Romaniuk, who's a product manager focused on model behavior. We're going be talking about GPT 5.1. What makes the model better? How they've been focusing on making its personality steerable and where they see things headed in the future.

For the first time ever, all of the models in chat are reasoning models.

Personality, though, for most of our users, I think is something much larger, and it's the whole experience of the model.

You should be able to get the experience that you want with chat.

Part of the art here is figuring out how to pull out these quirks of the model that can come across as personality without breaking steerability.

I'm very excited to talk about the models and how they've been changing over time. And using the word model also feels sort of funny now because it seems like there's so much more. And everything starts really in research. And when GPT-five 0.1 was being planned, what were the goals?

Yeah, for us, one of the main goals was to address all of the feedback we've been getting about GPT-five, but also we've been doing a lot of work to make the 5.5 instant into a reasoning model. So what the most exciting thing for, personally for me with the 5.1 release is that for the first time ever, all of the models in chat are reasoning models. So the model right now can decide to think, it's kind of what we say, it's like a chain of thought, and it'll decide how much it wants to think based on a prompt. If you're just saying like, hi to the model, what's up, it's not gonna be thinking, bu

Kind of what Daniel Kahneman calls like system one and system two thinking.

Yes. Having a reasoning model out for it as a default model for everyone just has a much smarter model. And I think with much smarter models, you just get improvements across the board, especially for things like instruction following. And for a lot of the use cases, people might not even think might require much reasoning, just that having improved intelligence, having the model actually think before it responds in certain queries just really helps. We've seen that improve evals across the board.