Reasoning Models Revolutionize AI Capabilities

This article discusses how new AI models like OpenAI's o1 and DeepSeek's R1 have changed the field of AI by focusing on multi-step reasoning rather than just increasing model size. These models use reinforcement learning to develop strategies for solving complex problems.

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For years, the prevailing assumption in AI was that increasing model size, data, and training compute would lead to more capable language models. However, two recent models - OpenAI's o1 and DeepSeek's R1 - have challenged this notion by showing the power of multi-step reasoning. These models use reinforcement learning to train the model to develop its own reasoning strategies, like breaking down problems, checking its work, and adjusting its approach mid-solution. This allows the models to leverage their inference-time compute much more effectively than previous approaches. The result is a dramatic improvement in performance on benchmarks like the American Invitational Mathematics Examination, where o1 scored in the 74th percentile of human test-takers compared to just 9% for the previous state-of-the-art GPT-4. The DeepSeek R1 paper provided important technical details on how this was achieved through reward-based training, without relying on human-labeled examples. This breakthrough in reasoning-focused AI models is poised to have a significant impact across many industries and applications.