Performance after post-training degrades if behavior gets too far from that of the base/SFT model (see Figure 1). Solving this issue would be an entirely different advancement from what o1-like post-training appears to do. So I expect that the model remains approximately as smart as the base model and the corresponding chatbot, it’s just better at packaging its intelligence into relevant long reasoning traces.
Interesting, I didn’t know that. But it seems like that assumes that o1′s special-sauce training can be viewed as a kind of RLHF, right? Do we know enough about that training to know that it’s RLHF-ish? Or at least some clearly offline approach.
Performance after post-training degrades if behavior gets too far from that of the base/SFT model (see Figure 1). Solving this issue would be an entirely different advancement from what o1-like post-training appears to do. So I expect that the model remains approximately as smart as the base model and the corresponding chatbot, it’s just better at packaging its intelligence into relevant long reasoning traces.
Interesting, I didn’t know that. But it seems like that assumes that o1′s special-sauce training can be viewed as a kind of RLHF, right? Do we know enough about that training to know that it’s RLHF-ish? Or at least some clearly offline approach.