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Notes -
This is very common. For a long time, practically every open model was a distilled knockoff trained from synthetic data, mostly from OpenAI. It's been so common that people are familiar with the marks this leaves on the model. Such models are worse than the model they're distilled from, typically less flexible out of distribution (e.g. obeying unusual system prompts, prompts, ...) and have an even more intense "sloppy" vibe to them. It's very common, and people have long gotten bored with these knockoff models. Before deepseek, I'd even say that it's all people expected from Chinese models.
It also doesn't match what we're seeing from R1 at all though. One of the reasons R1 is so impressive is that its slop level is much lower, its creativity is way higher, and it doesn't sound like any of the existing AI models. Even Claude feels straitjacketed in comparison, much less OpenAI Models.
I wouldn't be surprised if they did use synthetic data, but whatever training method they're using seems to do a great job of hiding it. Which is amazing in itself. It could have something to do with the reinforcement learning phase that they do. But regardless, it's definitely not as simple as training on data from OpenAI, because people have been doing that forever.
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