O1 Replication Journey -- Part 2: Surpassing O1-preview through Simple Distillation, Big Progress or Bitter Lesson?

• URL: http://arxiv.org/abs/2411.16489v1
• Date: Mon, 25 Nov 2024 15:31:27 GMT
• Title: O1 Replication Journey -- Part 2: Surpassing O1-preview through Simple Distillation, Big Progress or Bitter Lesson?
• Authors: Zhen Huang, Haoyang Zou, Xuefeng Li, Yixiu Liu, Yuxiang Zheng, Ethan Chern, Shijie Xia, Yiwei Qin, Weizhe Yuan, Pengfei Liu,
• Abstract summary: 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.
• Score: 30.87379989964516
• License:
• Abstract: This paper presents a critical examination of current approaches to replicating OpenAI's O1 model capabilities, with particular focus on the widespread but often undisclosed use of knowledge distillation techniques. While our previous work explored the fundamental technical path to O1 replication, this study reveals how simple distillation from O1's API, combined with supervised fine-tuning, can achieve superior performance on complex mathematical reasoning tasks. Through extensive experiments, we show that a base model fine-tuned on simply tens of thousands of samples O1-distilled long-thought chains outperforms O1-preview on the American Invitational Mathematics Examination (AIME) with minimal technical complexity. Moreover, our investigation extends beyond mathematical reasoning to explore the generalization capabilities of O1-distilled models across diverse tasks: hallucination, safety and open-domain QA. Notably, despite training only on mathematical problem-solving data, our models demonstrated strong generalization to open-ended QA tasks and became significantly less susceptible to sycophancy after fine-tuning. We deliberately make this finding public to promote transparency in AI research and to challenge the current trend of obscured technical claims in the field. Our work includes: (1) A detailed technical exposition of the distillation process and its effectiveness, (2) A comprehensive benchmark framework for evaluating and categorizing O1 replication attempts based on their technical transparency and reproducibility, (3) A critical discussion of the limitations and potential risks of over-relying on distillation approaches, our analysis culminates in a crucial bitter lesson: while the pursuit of more capable AI systems is important, the development of researchers grounded in first-principles thinking is paramount.

Related papers

• A Comparative Study on Reasoning Patterns of OpenAI's o1 Model [69.08287909042421]
  We show that OpenAI's o1 model has achieved the best performance on most datasets. We also provide a detailed analysis on several reasoning benchmarks.
  arXiv  Detail & Related papers  (2024-10-17T15:09:03Z)
• O1 Replication Journey: A Strategic Progress Report -- Part 1 [52.062216849476776]
  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.
  arXiv  Detail & Related papers  (2024-10-08T15:13:01Z)
• Evaluation of OpenAI o1: Opportunities and Challenges of AGI [112.0812059747033]
  o1-preview demonstrated remarkable capabilities, often achieving human-level or superior performance. The model excelled in tasks requiring intricate reasoning and knowledge integration across various fields. Overall results indicate significant progress towards artificial general intelligence.
  arXiv  Detail & Related papers  (2024-09-27T06:57:00Z)
• Gradient based Feature Attribution in Explainable AI: A Technical Review [13.848675695545909]
  Surge in black-box AI models has prompted the need to explain the internal mechanism and justify their reliability. gradient based explanations can be directly adopted for neural network models. We introduce both human and quantitative evaluations to measure algorithm performance.
  arXiv  Detail & Related papers  (2024-03-15T15:49:31Z)
• A Systematic Literature Review on Explainability for Machine/Deep Learning-based Software Engineering Research [23.966640472958105]
  This paper presents a systematic literature review of approaches that aim to improve the explainability of AI models within the context of Software Engineering. We aim to summarize the SE tasks where XAI techniques have shown success to date; (2) classify and analyze different XAI techniques; and (3) investigate existing evaluation approaches.
  arXiv  Detail & Related papers  (2024-01-26T03:20:40Z)
• Understanding, Predicting and Better Resolving Q-Value Divergence in Offline-RL [86.0987896274354]
  We first identify a fundamental pattern, self-excitation, as the primary cause of Q-value estimation divergence in offline RL. We then propose a novel Self-Excite Eigenvalue Measure (SEEM) metric to measure the evolving property of Q-network at training. For the first time, our theory can reliably decide whether the training will diverge at an early stage.
  arXiv  Detail & Related papers  (2023-10-06T17:57:44Z)
• Counterfactual Explanations as Interventions in Latent Space [62.997667081978825]
  Counterfactual explanations aim to provide to end users a set of features that need to be changed in order to achieve a desired outcome. Current approaches rarely take into account the feasibility of actions needed to achieve the proposed explanations. We present Counterfactual Explanations as Interventions in Latent Space (CEILS), a methodology to generate counterfactual explanations.
  arXiv  Detail & Related papers  (2021-06-14T20:48:48Z)
• Simple Unsupervised Similarity-Based Aspect Extraction [0.9558392439655015]
  We propose a simple approach called SUAEx for aspect extraction. SUAEx is unsupervised and relies solely on the similarity of word embeddings. Experimental results on datasets from three different domains have shown that SUAEx achieves results that can outperform the state-of-the-art attention-based approach at a fraction of the time.
  arXiv  Detail & Related papers  (2020-08-25T04:58:07Z)
• An Information Bottleneck Approach for Controlling Conciseness in Rationale Extraction [84.49035467829819]
  We show that it is possible to better manage this trade-off by optimizing a bound on the Information Bottleneck (IB) objective. Our fully unsupervised approach jointly learns an explainer that predicts sparse binary masks over sentences, and an end-task predictor that considers only the extracted rationale.
  arXiv  Detail & Related papers  (2020-05-01T23:26:41Z)

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.