Researchers have developed a new generation of AI models that can dynamically adjust their computational effort based on problem complexity, representing a major shift in how artificial intelligence approaches challenging tasks.
The technology, exemplified by models like DeepSeek-R1 and OpenAI's o-series, employs what developers call a "reasoning-first approach" that prioritizes thorough analysis over quick pattern matching. DeepSeek-R1 is built with this reasoning-first methodology, making it particularly well-suited for tackling complex tasks in science, coding, and mathematics through advanced logical inference and problem-solving. This focus on "thinking before answering" makes it especially valuable for technical applications.
Unlike conventional AI systems, these new reasoning models are trained to "think for longer" before responding. OpenAI's o3, for example, can break down difficult questions into logical steps, perform intermediate calculations or tool calls, and then produce well-founded answers. Being reasoning models, they effectively fact-check themselves, which helps avoid pitfalls that typically trip up standard models. While they take seconds to minutes longer to arrive at solutions compared to typical non-reasoning models, they tend to be more reliable in domains such as physics, science, and math.
OpenAI has observed that large-scale reinforcement learning exhibits the same "more compute = better performance" trend seen in earlier model training. By retracing the scaling path—this time in reinforcement learning—they've pushed an additional order of magnitude in both training compute and inference-time reasoning, with clear performance gains validating that the models' performance continues to improve the more they're allowed to think.
These models actively generate multiple solution paths during inference, evaluating each with the help of integrated evaluator models to determine the most promising option. By training the evaluator on expert-labeled data, developers ensure that the models develop a strong capacity to reason through complex, multi-step problems. This feature enables the model to act as a judge of its own reasoning, moving large language models closer to being able to "think" rather than simply respond.
DeepSeek's approach combines chain-of-thought reasoning with reinforcement learning in which an autonomous agent learns to perform a task through trial and error without human instructions. This questions the assumption that models will improve their reasoning ability solely by training on labeled examples of correct behavior. As one researcher put it: "Can we just reward the model for correctness and let it discover the best way to think on its own?"
The implications for real-world applications are profound. These models could transform how AI handles complex problems in fields ranging from scientific research and engineering to business strategy and creative problem-solving. By allocating computational resources proportionally to task difficulty—similar to how humans naturally spend more time on harder problems—these systems promise more reliable performance on the most challenging intellectual tasks humanity faces.