Pre-training and unsupervised learning enhance the model's broad-based intelligence and improve its generalization capabilities, complementing reasoning abilities that can be more variable.

The emergence of reasoning models is expected to enable AI agents to perform actions on behalf of users, enhancing their reliability and effectiveness in real-world applications.

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.

In 2025, reasoning will become the defining focus in AI development, as pre-training reaches diminishing returns.

The outcome is better if you structure the reasoning and help iterate with the model on how it should do reasoning.

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.

Sam Altman has suggested that unsupervised pre-training models like GPT-4.5 and specialized reasoning-focused models will converge into a unified architecture, which we might see in GPT-5.

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

Structuring the reasoning in prompts helps the model perform better, even if it's not strictly reasoning as we define it.