Movatterモバイル変換


[0]ホーム

URL:


Skip to main content
Cornell University

Monday, May 5: arXiv will be READ ONLY at 9:00AM EST for approximately 30 minutes. We apologize for any inconvenience.

We gratefully acknowledge support from the Simons Foundation,member institutions, and all contributors.Donate
arxiv logo>cs> arXiv:2410.18982
arXiv logo
Cornell University Logo

Computer Science > Artificial Intelligence

arXiv:2410.18982 (cs)
[Submitted on 8 Oct 2024]

Title:O1 Replication Journey: A Strategic Progress Report -- Part 1

View PDFHTML (experimental)
Abstract:This paper introduces a pioneering approach to artificial intelligence research, embodied in our O1 Replication Journey. In response to the announcement of OpenAI's groundbreaking O1 model, we embark on a transparent, real-time exploration to replicate its capabilities while reimagining the process of conducting and communicating AI research. Our methodology addresses critical challenges in modern AI research, including the insularity of prolonged team-based projects, delayed information sharing, and the lack of recognition for diverse contributions. By providing comprehensive, real-time documentation of our replication efforts, including both successes and failures, we aim to foster open science, accelerate collective advancement, and lay the groundwork for AI-driven scientific discovery. Our research progress report diverges significantly from traditional research papers, offering continuous updates, full process transparency, and active community engagement throughout the research journey. Technologically, we proposed the journey learning paradigm, which encourages models to learn not just shortcuts, but the complete exploration process, including trial and error, reflection, and backtracking. With only 327 training samples and without any additional tricks, journey learning outperformed conventional supervised learning by over 8\% on the MATH dataset, demonstrating its extremely powerful potential. We believe this to be the most crucial component of O1 technology that we have successfully decoded. We share valuable resources including technical hypotheses and insights, cognitive exploration maps, custom-developed tools, etc atthis https URL.
Subjects:Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
Cite as:arXiv:2410.18982 [cs.AI]
 (orarXiv:2410.18982v1 [cs.AI] for this version)
 https://doi.org/10.48550/arXiv.2410.18982
arXiv-issued DOI via DataCite

Submission history

From: Yiwei Qin [view email]
[v1] Tue, 8 Oct 2024 15:13:01 UTC (2,308 KB)
Full-text links:

Access Paper:

Current browse context:
cs.AI
Change to browse by:
export BibTeX citation

Bookmark

BibSonomy logoReddit logo

Bibliographic and Citation Tools

Bibliographic Explorer(What is the Explorer?)
Connected Papers(What is Connected Papers?)
scite Smart Citations(What are Smart Citations?)

Code, Data and Media Associated with this Article

CatalyzeX Code Finder for Papers(What is CatalyzeX?)
Hugging Face(What is Huggingface?)
Papers with Code(What is Papers with Code?)

Demos

Hugging Face Spaces(What is Spaces?)

Recommenders and Search Tools

Influence Flower(What are Influence Flowers?)
CORE Recommender(What is CORE?)

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community?Learn more about arXivLabs.

Which authors of this paper are endorsers? |Disable MathJax (What is MathJax?)

[8]ページ先頭

©2009-2025 Movatter.jp