We believe that a hidden chain of thought presents a unique opportunity for monitoring models. Assuming it is faithful and legible, the hidden chain of thought allows us to "read the mind" of the model and understand its thought process. For example, in the future we may wish to monitor the chain of thought for signs of manipulating the user. However, for this to work the model must have freedom to express its thoughts in unaltered form, so we cannot train any policy compliance or user preferences onto the chain of thought. We also do not want to make an unaligned chain of thought directly visible to users.
Therefore, after weighing multiple factors including user experience, competitive advantage, and the option to pursue the chain of thought monitoring, we have decided not to show the raw chains of thought to users. We acknowledge this decision has disadvantages. We strive to partially make up for it by teaching the model to reproduce any useful ideas from the chain of thought in the answer. For the o1 model series we show a model-generated summary of the chain of thought.
o1 significantly advances the state-of-the-art in AI reasoning. We plan to release improved versions of this model as we continue iterating. We expect these new reasoning capabilities will improve our ability to align models to human values and principles. We believe o1 – and its successors – will unlock many new use cases for AI in science, coding, math, and related fields. We are excited for users and API developers to discover how it can improve their daily work.
Dataset | Metric | gpt-4o | o1-preview | o1 |
---|---|---|---|---|
Competition Math AIME (2024) | cons@64 | 13.4 | 56.7 | 83.3 |
pass@1 | 9.3 | 44.6 | 74.4 | |
Competition Code CodeForces | Elo | 808 | 1,258 | 1,673 |
Percentile | 11.0 | 62.0 | 89.0 | |
GPQA Diamond | cons@64 | 56.1 | 78.3 | 78.0 |
pass@1 | 50.6 | 73.3 | 77.3 | |
Biology | cons@64 | 63.2 | 73.7 | 68.4 |
pass@1 | 61.6 | 65.9 | 69.2 | |
Chemistry | cons@64 | 43.0 | 60.2 | 65.6 |
pass@1 | 40.2 | 59.9 | 64.7 | |
Physics | cons@64 | 68.6 | 89.5 | 94.2 |
pass@1 | 59.5 | 89.4 | 92.8 | |
MATH | pass@1 | 60.3 | 85.5 | 94.8 |
MMLU | pass@1 | 88.0 | 92.3 | 90.8 |
MMMU (val) | pass@1 | 69.1 | n/a | 78.2 | pass@1 | 63.8 | n/a | 73.9 |
https://www.anthropic.com/news/claude-3-5-sonnet (opens in a new window) , https://deepmind.google/technologies/gemini/pro (opens in a new window)
Our evaluations used the same 500 problem test split found in https://arxiv.org/abs/2305.20050 (opens in a new window)
https://codeforces.com/blog/entry/68288 (opens in a new window)
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