Monte-Carlo research and the “Test Time Compute”.
In fact, this AI is designed to surpass its predecessors in terms of reasoning and complex problem-solving capabilities. And the least we can say is that this model is efficient enough to come close, without reaching it, to the status of AGI (Artificial General Intelligence), that is to say an AI comparable to that of a human: capable of dealing with any problem and learning. O3, although very efficient, remains specialized in specific areas and well-defined tasks, in particular thanks to its multimodal skills and its algorithms optimized for particular scenarios.
Passed through the famous Arc-AGI test, the model outperforms competing models, but also humans. The tests highlight AI’s ability to identify complex patterns and deduce logical solutions. It involves solving problems in abstract logic, a field often used to measure human intelligence.
A probabilistic exploration
O3 achieved a score of 87.5%, surpassing the estimated human average of 85%, with energy consumption three times lower than the previous model, o1. This result reflects a significant advance in algorithm optimization, showing that performance can be increased without a disproportionate increase in required resources. To achieve this performance, the model benefits from a slightly different architecture than the publisher’s other models. Certainly, it is based on LLM training, but it also benefits from specific techniques such as Monte-Carlo search and the use of “Test Time Compute”.
Monte Carlo search is a method from applied mathematics, frequently used to solve problems that require an exhaustive exploration of multiple possibilities. This approach relies on repeated and random simulations in order to identify the best solutions in a given search space. Concretely, o3 creates a set of sample solutions for a given task. These samples are then analyzed and compared according to defined criteria, allowing the system to choose the most relevant answer. This approach is particularly effective for complex or ambiguous tasks where it is difficult to determine an optimal response in a single attempt.
Leave time for reflection
“Test Time Compute” is an approach that involves providing more resources and time for the model to “think”. This involves increasing the time and resources allocated to a model to solve a specific task during its inference phase (i.e. when it is used, not during training). Rather than settling for a quick, one-time treatment of a problem, the model explores pathways to a response longer and more intensely before responding. He proceeds through successive cycles of analysis and resolution, gradually refining his response. The model can break the problem into subtasks, solve them separately, and then integrate the results. This reflects a resolution method close to that of a human. The only drawback to this method is a longer response time than the average of current models.
By amalgamating the two methods, Monte-Carlo and “Test Time Compute”, OpenAI addresses a major constraint in the field of AI: the limits of traditional methods of improving relevance through scaling, which mainly rely on increasing the size of models and datasets for training. The more varied and numerous a model is trained on data, the better its generalization capacity. However, with the scarcity of usable data and the explosion of costs associated with ever-larger models, the relevance of scaling models is reaching its limits. Vendors are incorporating alternative progression methods to continue improving the capabilities of AI systems.
Synergy between Monte-Carlo and Test Time Compute
These two concepts work synergistically to maximize the effectiveness of o3. Monte Carlo search generates a diversity of possible solutions, while Test Time Compute optimizes the evaluation and selection process by dynamically allocating the necessary resources. This combination is particularly useful for benchmarks like ARC-AGI, where tasks require advanced dispositions for reasoning and deep contextual understanding.
Using this combination in o3 gives it the ability to overcome the limitations of traditional models. This approach by deepening reasoning not only increases accuracy, it also introduces a form of “meta-reasoning” where the model dynamically adapts its “thinking” according to the requirements of the task. This brings AI closer to a more flexible and general form of intelligence, but o3’s design instead reflects a desire to delve deeper into specialized capabilities rather than aiming for omnidirectional intelligence. Therefore, it is more of a tool for professionals than a model aimed at universal cognition. Aware of the problems that this complete model may pose in terms of response time, OpenAI announced the marketing at the beginning of 2025 of o3 mini, a smaller model “distilled” from o3.
