OpenAI recently released the o1-preview and o1-mini models, trained to emulate reasoning with a focus on the “Chain-of-Thought” (CoT) paradigm. These models show impressive results on some benchmarks but struggle on others, showcasing the need for further research in AGI development. The models use reinforcement learning to refine reasoning tokens and have a unique test-time scaling technique that allows for adaptation to novelty. While the models represent a shift towards memorizing reasoning rather than just answers, they still primarily rely on pre-training data distribution. The push towards efficiency in AGI development is crucial, as seen in the ARC Prize testing results. Join the ARC Prize community to contribute to open AGI development and potentially win prizes for innovative solutions.
https://arcprize.org/blog/openai-o1-results-arc-prize