I think this is a misleading clickbait title. It references a popular article with the same misleading clickbait title, and the only thing that popular article references is a youtube video with the misleading clickbait title, “Chinese Researchers Just CRACKED OpenAI’s AGI Secrets!”
However, the description of that youtube video does reference the paper in question and a twitter thread describing this paper:
Nothing is “cracked” here. It’s just a roadmap which might work or not, depending on luck and efforts. It might correspond to what’s under the hood of o1 models or not (never mind o3, the paper is published a couple of days before the o3 announcement).
The abstract of the paper ends with
“Existing open-source projects that attempt to reproduce o1 can be seem as a part or a variant of our roadmap. Collectively, these components underscore how learning and search drive o1′s advancement, making meaningful contributions to the development of LLM.”
The abstract also has distinct feeling of being written by an LLM. The whole paper is just a discussion of various things one could try if one wants to reproduce o1. It also references a number of open source and closed source implementations of reasoners over LLMs. There are no new technical advances in the paper.
I think this is a misleading clickbait title. It references a popular article with the same misleading clickbait title, and the only thing that popular article references is a youtube video with the misleading clickbait title, “Chinese Researchers Just CRACKED OpenAI’s AGI Secrets!”
However, the description of that youtube video does reference the paper in question and a twitter thread describing this paper:
Scaling of Search and Learning: A Roadmap to Reproduce o1 from Reinforcement Learning Perspective, https://arxiv.org/abs/2412.14135
https://x.com/rohanpaul_ai/status/1872713137407049962
Nothing is “cracked” here. It’s just a roadmap which might work or not, depending on luck and efforts. It might correspond to what’s under the hood of o1 models or not (never mind o3, the paper is published a couple of days before the o3 announcement).
The abstract of the paper ends with
The abstract also has distinct feeling of being written by an LLM. The whole paper is just a discussion of various things one could try if one wants to reproduce o1. It also references a number of open source and closed source implementations of reasoners over LLMs. There are no new technical advances in the paper.