o1

For years, the dominant paradigm in large language model improvement was scaling: more parameters, more data, more compute during training. OpenAI's o1, released in preview on September 12, 2024, represented a deliberate shift in that paradigm. Rather than simply predicting the next token as fast as possible, o1 was trained to spend additional compute at inference time generating an internal chain of thought before producing its final answer. This 'thinking before answering' approach drew on ideas from the chain-of-thought prompting literature but embedded the behavior directly into the model through reinforcement learning.

o1 reframed the central question of AI capability research. Before o1, the field largely asked 'how do we build a bigger, better-trained model?' After o1, an equally important question became 'how do we let a model think longer and more carefully at inference time?' This shift opened an entirely new design space — one where capability is not fixed at the end of a training run but can be dynamically allocated based on task difficulty. It also provided a compelling existence proof that reinforcement learning, without human-annotated reasoning chains, could produce sophisticated internal reasoning strategies.