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

• URL: http://arxiv.org/abs/2410.18982v1
• Date: Tue, 08 Oct 2024 15:13:01 GMT
• Title: O1 Replication Journey: A Strategic Progress Report -- Part 1
• Authors: Yiwei Qin, Xuefeng Li, Haoyang Zou, Yixiu Liu, Shijie Xia, Zhen Huang, Yixin Ye, Weizhe Yuan, Hector Liu, Yuanzhi Li, Pengfei Liu,
• Abstract summary: This paper introduces a pioneering approach to artificial intelligence research, embodied in our O1 Replication Journey. Our methodology addresses critical challenges in modern AI research, including the insularity of prolonged team-based projects. We propose the journey learning paradigm, which encourages models to learn not just shortcuts, but the complete exploration process.
• Score: 52.062216849476776
• License:
• 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 at https://github.com/GAIR-NLP/O1-Journey.

Related papers

• O1 Replication Journey -- Part 2: Surpassing O1-preview through Simple Distillation, Big Progress or Bitter Lesson? [30.87379989964516]
  This paper presents a critical examination of current approaches to replicating OpenAI's O1 model capabilities. We show how simple distillation from O1's API, combined with supervised fine-tuning, can achieve superior performance on complex mathematical reasoning tasks.
  arXiv  Detail & Related papers  (2024-11-25T15:31:27Z)
• KOI: Accelerating Online Imitation Learning via Hybrid Key-state Guidance [51.09834120088799]
  We introduce the hybrid Key-state guided Online Imitation (KOI) learning method. We use visual-language models to extract semantic key states from expert trajectory, indicating the objectives of "what to do" Within the intervals between semantic key states, optical flow is employed to capture motion key states to understand the mechanisms of "how to do"
  arXiv  Detail & Related papers  (2024-08-06T02:53:55Z)
• Towards Data-Centric Automatic R&D [17.158255487686997]
  Researchers often seek the potential research directions by reading and then verifying them through experiments. The data-driven black-box deep learning method has demonstrated its effectiveness in a wide range of real-world scenarios. We propose a Real-world Data-centric automatic R&D Benchmark, namely RD2Bench.
  arXiv  Detail & Related papers  (2024-04-17T11:33:21Z)
• A Closer Look at the Limitations of Instruction Tuning [52.587607091917214]
  We show that Instruction Tuning (IT) fails to enhance knowledge or skills in large language models (LLMs) We also show that popular methods to improve IT do not lead to performance improvements over a simple LoRA fine-tuned model. Our findings reveal that responses generated solely from pre-trained knowledge consistently outperform responses by models that learn any form of new knowledge from IT on open-source datasets.
  arXiv  Detail & Related papers  (2024-02-03T04:45:25Z)
• Deep Active Learning for Computer Vision: Past and Future [50.19394935978135]
  Despite its indispensable role for developing AI models, research on active learning is not as intensive as other research directions. By addressing data automation challenges and coping with automated machine learning systems, active learning will facilitate democratization of AI technologies.
  arXiv  Detail & Related papers  (2022-11-27T13:07:14Z)
• Unveiling the Tapestry: the Interplay of Generalization and Forgetting in Continual Learning [18.61040106667249]
  In AI, generalization refers to a model's ability to perform well on out-of-distribution data related to a given task, beyond the data it was trained on. Continual learning methods often include mechanisms to mitigate catastrophic forgetting, ensuring that knowledge from earlier tasks is retained. We introduce a simple and effective technique known as Shape-Texture Consistency Regularization (STCR), which caters to continual learning.
  arXiv  Detail & Related papers  (2022-11-21T04:36:24Z)
• General Intelligence Requires Rethinking Exploration [24.980249597326985]
  We argue that exploration is essential to all learning systems, including supervised learning. Generalized exploration serves as a necessary objective for maintaining open-ended learning processes.
  arXiv  Detail & Related papers  (2022-11-15T00:46:15Z)
• A curated, ontology-based, large-scale knowledge graph of artificial intelligence tasks and benchmarks [4.04540578484476]
  Intelligence Task Ontology and Knowledge Graph (ITO) is a comprehensive resource on artificial intelligence tasks, benchmark results and performance metrics. ITO is a richly structured and manually curated resource on artificial intelligence tasks, benchmark results and performance metrics. The goal of ITO is to enable precise and network-based analyses of the global landscape of AI tasks and capabilities.
  arXiv  Detail & Related papers  (2021-10-04T13:25:53Z)
• Knowledge-Aware Procedural Text Understanding with Multi-Stage Training [110.93934567725826]
  We focus on the task of procedural text understanding, which aims to comprehend such documents and track entities' states and locations during a process. Two challenges, the difficulty of commonsense reasoning and data insufficiency, still remain unsolved. We propose a novel KnOwledge-Aware proceduraL text understAnding (KOALA) model, which effectively leverages multiple forms of external knowledge.
  arXiv  Detail & Related papers  (2020-09-28T10:28:40Z)
• Planning to Explore via Self-Supervised World Models [120.31359262226758]
  Plan2Explore is a self-supervised reinforcement learning agent. We present a new approach to self-supervised exploration and fast adaptation to new tasks. Without any training supervision or task-specific interaction, Plan2Explore outperforms prior self-supervised exploration methods.
  arXiv  Detail & Related papers  (2020-05-12T17:59:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.