How do thinking and reasoning models work?

The podcast discusses thinking models in large language models (LLMs), explaining how they achieve better results in complex tasks through increased token usage and a summary of the thinking trace. It covers scaling laws, test-time compute, and chain-of-thought prompting, illustrating how generating intermediate steps improves accuracy. The podcast explores strategies like "best of N," reward models, and reinforcement learning to enhance model reasoning and the generation of longer, more effective chains of thought. It also touches on the combination of supervised fine-tuning and reinforcement learning to improve a model's thinking abilities.