Reasoning is expected to be a core capability of future models, and the two paradigms of unsupervised pre-training and specialized reasoning complement each other well.

Pre-training compresses data by identifying connections and analogies, while reasoning focuses on specific problems, requiring careful thought to unlock various problem-solving skills.

The ability to allow AI models to reason for extended periods, rather than just providing quick answers, can lead to significantly better outcomes for complex questions.

Pre-training is inherently broad and diverse, making it challenging to achieve the same breadth when using reinforcement learning in specific environments.

Achieving AGI requires not just pre-training but also the ability to reason and utilize additional computational resources effectively to improve outcomes.

The addition of a reasoning paradigm to AI models can significantly enhance their performance, as demonstrated by the difference in capabilities between versions of AlphaGo with and without this feature.

The shift from merely scaling LLMs to incorporating reasoning techniques has opened new avenues for AI development, allowing models to perform better by thinking longer and more deeply.

The emergence of reasoning behaviors in AI models can occur without human preferences being explicitly added during post-training, showcasing the power of reinforcement learning.

The DeepSeek paper detailed that they used reasoning-heavy but standard post-training techniques after large-scale reasoning training.