OpenAI, "Learning to Reason with LLMs" (2024).
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Rating inherited from publication venue: OpenAI
The o1 model card and research blog post is a landmark capabilities announcement relevant to AI safety because it introduces deliberative reasoning at scale, changing assumptions about model behavior, evaluation difficulty, and the pace of capability gains.
Metadata
Summary
OpenAI introduces the o1 model series, which uses reinforcement learning to train large language models to reason through complex problems via extended chain-of-thought before responding. The models demonstrate significantly improved performance on challenging benchmarks in mathematics, coding, and scientific reasoning. This represents a major capability advance with implications for both AI applications and AI safety evaluation.
Key Points
- •o1 uses reinforcement learning to develop internal chain-of-thought reasoning, allowing the model to 'think' longer on harder problems before answering.
- •Achieves state-of-the-art results on competition math (AIME), coding (Codeforces), and PhD-level science questions (GPQA), surpassing prior models significantly.
- •The extended reasoning process can be partially inspected but the internal chain-of-thought is distinct from the final visible response, raising interpretability questions.
- •Improved reasoning capability correlates with improved safety behavior in some evaluations, but also introduces new risks around more capable autonomous action.
- •Represents a step toward models that can engage in extended deliberative reasoning, a key threshold in AI capability development.
Cited by 1 page
| Page | Type | Quality |
|---|---|---|
| Large Language Models | Capability | 60.0 |
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